{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 工具导入和数据读取\n",
    "\n",
    "### 工具导入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    " \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据读取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "读取数据集\n",
    "\"\"\"\n",
    "test_data = pd.read_csv('./data_format1/test_format1.csv')\n",
    "train_data = pd.read_csv('./data_format1/train_format1.csv')\n",
    "\n",
    "user_info = pd.read_csv('./data_format1/user_info_format1.csv')\n",
    "user_log = pd.read_csv('./data_format1/user_log_format1.csv')\n",
    "\n",
    "#user_info = pd.read_csv('./data_format1/user_info_format1.csv').drop_duplicates()\n",
    "#user_log = pd.read_csv('./data_format1/user_log_format1.csv').rename(columns={\"seller_id\":'merchant_id'})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据集样例查看"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>merchant_id</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34176</td>\n",
       "      <td>3906</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34176</td>\n",
       "      <td>121</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34176</td>\n",
       "      <td>4356</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34176</td>\n",
       "      <td>2217</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>230784</td>\n",
       "      <td>4818</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  merchant_id  label\n",
       "0    34176         3906      0\n",
       "1    34176          121      0\n",
       "2    34176         4356      1\n",
       "3    34176         2217      0\n",
       "4   230784         4818      0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>merchant_id</th>\n",
       "      <th>prob</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>163968</td>\n",
       "      <td>4605</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>360576</td>\n",
       "      <td>1581</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>98688</td>\n",
       "      <td>1964</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>98688</td>\n",
       "      <td>3645</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>295296</td>\n",
       "      <td>3361</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  merchant_id  prob\n",
       "0   163968         4605   NaN\n",
       "1   360576         1581   NaN\n",
       "2    98688         1964   NaN\n",
       "3    98688         3645   NaN\n",
       "4   295296         3361   NaN"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>age_range</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>376517</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>234512</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>344532</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>186135</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30230</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  age_range  gender\n",
       "0   376517        6.0     1.0\n",
       "1   234512        5.0     0.0\n",
       "2   344532        5.0     0.0\n",
       "3   186135        5.0     0.0\n",
       "4    30230        5.0     0.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_info.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>cat_id</th>\n",
       "      <th>seller_id</th>\n",
       "      <th>brand_id</th>\n",
       "      <th>time_stamp</th>\n",
       "      <th>action_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>328862</td>\n",
       "      <td>323294</td>\n",
       "      <td>833</td>\n",
       "      <td>2882</td>\n",
       "      <td>2661.0</td>\n",
       "      <td>829</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>328862</td>\n",
       "      <td>844400</td>\n",
       "      <td>1271</td>\n",
       "      <td>2882</td>\n",
       "      <td>2661.0</td>\n",
       "      <td>829</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>328862</td>\n",
       "      <td>575153</td>\n",
       "      <td>1271</td>\n",
       "      <td>2882</td>\n",
       "      <td>2661.0</td>\n",
       "      <td>829</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>328862</td>\n",
       "      <td>996875</td>\n",
       "      <td>1271</td>\n",
       "      <td>2882</td>\n",
       "      <td>2661.0</td>\n",
       "      <td>829</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>328862</td>\n",
       "      <td>1086186</td>\n",
       "      <td>1271</td>\n",
       "      <td>1253</td>\n",
       "      <td>1049.0</td>\n",
       "      <td>829</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  item_id  cat_id  seller_id  brand_id  time_stamp  action_type\n",
       "0   328862   323294     833       2882    2661.0         829            0\n",
       "1   328862   844400    1271       2882    2661.0         829            0\n",
       "2   328862   575153    1271       2882    2661.0         829            0\n",
       "3   328862   996875    1271       2882    2661.0         829            0\n",
       "4   328862  1086186    1271       1253    1049.0         829            0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_log.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 单变量数据分析\n",
    "\n",
    "### 数据类型和数据大小\n",
    "\n",
    "**用户信息数据**\n",
    "\n",
    "1. 数据集中共有2个float64类型和1个int64类型的数据\n",
    "2. 数据大小9.7MB\n",
    "3. 数据集共有424170条数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 424170 entries, 0 to 424169\n",
      "Data columns (total 3 columns):\n",
      " #   Column     Non-Null Count   Dtype  \n",
      "---  ------     --------------   -----  \n",
      " 0   user_id    424170 non-null  int64  \n",
      " 1   age_range  421953 non-null  float64\n",
      " 2   gender     417734 non-null  float64\n",
      "dtypes: float64(2), int64(1)\n",
      "memory usage: 9.7 MB\n"
     ]
    }
   ],
   "source": [
    "user_info.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**用户行为数据**\n",
    "\n",
    "1. 数据集中共有6个int64类型和1个float64类型的数据\n",
    "2. 数据大小2.9GB\n",
    "3. 数据集共有54925330条数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 54925330 entries, 0 to 54925329\n",
      "Data columns (total 7 columns):\n",
      " #   Column       Dtype  \n",
      "---  ------       -----  \n",
      " 0   user_id      int64  \n",
      " 1   item_id      int64  \n",
      " 2   cat_id       int64  \n",
      " 3   seller_id    int64  \n",
      " 4   brand_id     float64\n",
      " 5   time_stamp   int64  \n",
      " 6   action_type  int64  \n",
      "dtypes: float64(1), int64(6)\n",
      "memory usage: 2.9 GB\n"
     ]
    }
   ],
   "source": [
    "user_log.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**用户购买训练数据**\n",
    "\n",
    "1. 数据均为int64类型\n",
    "2. 数据大小6MB\n",
    "3. 数据集共有260864条数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 260864 entries, 0 to 260863\n",
      "Data columns (total 3 columns):\n",
      " #   Column       Non-Null Count   Dtype\n",
      "---  ------       --------------   -----\n",
      " 0   user_id      260864 non-null  int64\n",
      " 1   merchant_id  260864 non-null  int64\n",
      " 2   label        260864 non-null  int64\n",
      "dtypes: int64(3)\n",
      "memory usage: 6.0 MB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 缺失值查看"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**用户信息数据缺失**\n",
    "\n",
    "**年龄缺失**：\n",
    "\n",
    "1. 年龄值为空的缺失率为0.5%\n",
    "2. 年龄值缺失或者年龄值为缺省值0\n",
    "3. 共计95131条数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.005226677982884221"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(user_info.shape[0]-user_info['age_range'].count())/user_info.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id      95131\n",
       "age_range    92914\n",
       "gender       90664\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_info[user_info['age_range'].isna() | (user_info['age_range'] == 0)].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_range</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>92914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>52871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>111654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>79991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>40777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>35464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>6992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>1266</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           user_id\n",
       "age_range         \n",
       "0.0          92914\n",
       "1.0             24\n",
       "2.0          52871\n",
       "3.0         111654\n",
       "4.0          79991\n",
       "5.0          40777\n",
       "6.0          35464\n",
       "7.0           6992\n",
       "8.0           1266"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_info.groupby(['age_range'])[['user_id']].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**性别缺失**\n",
    "1. 性别值为空的缺失率 1.5%\n",
    "2. 性别值缺失或者性别为缺省值2\n",
    "3. 共计16862条数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.01517316170403376"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(user_info.shape[0]-user_info['gender'].count())/user_info.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id      16862\n",
       "age_range    14664\n",
       "gender       10426\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_info[user_info['gender'].isna() | (user_info['gender'] == 2)].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>285638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>121670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>10426</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        user_id\n",
       "gender         \n",
       "0.0      285638\n",
       "1.0      121670\n",
       "2.0       10426"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_info.groupby(['gender'])[['user_id']].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**年龄或者性别其中有一个有缺失**\n",
    "\n",
    "共计106330条数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id      106330\n",
       "age_range    104113\n",
       "gender        99894\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_info[user_info['age_range'].isna() | (user_info['age_range'] == 0) | user_info['gender'].isna() | (user_info['gender'] == 2)].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**用户行为日志信息**\n",
    "\n",
    "brand_id字段有91015条缺失数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id            0\n",
       "item_id            0\n",
       "cat_id             0\n",
       "seller_id          0\n",
       "brand_id       91015\n",
       "time_stamp         0\n",
       "action_type        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_log.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 观察数据分布\n",
    "\n",
    "### 整体数据统计信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>age_range</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>424170.000000</td>\n",
       "      <td>421953.000000</td>\n",
       "      <td>417734.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>212085.500000</td>\n",
       "      <td>2.930262</td>\n",
       "      <td>0.341179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>122447.476179</td>\n",
       "      <td>1.942978</td>\n",
       "      <td>0.524112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>106043.250000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>212085.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>318127.750000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>424170.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             user_id      age_range         gender\n",
       "count  424170.000000  421953.000000  417734.000000\n",
       "mean   212085.500000       2.930262       0.341179\n",
       "std    122447.476179       1.942978       0.524112\n",
       "min         1.000000       0.000000       0.000000\n",
       "25%    106043.250000       2.000000       0.000000\n",
       "50%    212085.500000       3.000000       0.000000\n",
       "75%    318127.750000       4.000000       1.000000\n",
       "max    424170.000000       8.000000       2.000000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_info.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>cat_id</th>\n",
       "      <th>seller_id</th>\n",
       "      <th>brand_id</th>\n",
       "      <th>time_stamp</th>\n",
       "      <th>action_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.492533e+07</td>\n",
       "      <td>5.492533e+07</td>\n",
       "      <td>5.492533e+07</td>\n",
       "      <td>5.492533e+07</td>\n",
       "      <td>5.483432e+07</td>\n",
       "      <td>5.492533e+07</td>\n",
       "      <td>5.492533e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.121568e+05</td>\n",
       "      <td>5.538613e+05</td>\n",
       "      <td>8.770308e+02</td>\n",
       "      <td>2.470941e+03</td>\n",
       "      <td>4.153348e+03</td>\n",
       "      <td>9.230953e+02</td>\n",
       "      <td>2.854458e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.222872e+05</td>\n",
       "      <td>3.221459e+05</td>\n",
       "      <td>4.486269e+02</td>\n",
       "      <td>1.473310e+03</td>\n",
       "      <td>2.397679e+03</td>\n",
       "      <td>1.954305e+02</td>\n",
       "      <td>8.075806e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>5.110000e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.063360e+05</td>\n",
       "      <td>2.731680e+05</td>\n",
       "      <td>5.550000e+02</td>\n",
       "      <td>1.151000e+03</td>\n",
       "      <td>2.027000e+03</td>\n",
       "      <td>7.300000e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.126540e+05</td>\n",
       "      <td>5.555290e+05</td>\n",
       "      <td>8.210000e+02</td>\n",
       "      <td>2.459000e+03</td>\n",
       "      <td>4.065000e+03</td>\n",
       "      <td>1.010000e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.177500e+05</td>\n",
       "      <td>8.306890e+05</td>\n",
       "      <td>1.252000e+03</td>\n",
       "      <td>3.760000e+03</td>\n",
       "      <td>6.196000e+03</td>\n",
       "      <td>1.109000e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.241700e+05</td>\n",
       "      <td>1.113166e+06</td>\n",
       "      <td>1.671000e+03</td>\n",
       "      <td>4.995000e+03</td>\n",
       "      <td>8.477000e+03</td>\n",
       "      <td>1.112000e+03</td>\n",
       "      <td>3.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            user_id       item_id        cat_id     seller_id      brand_id  \\\n",
       "count  5.492533e+07  5.492533e+07  5.492533e+07  5.492533e+07  5.483432e+07   \n",
       "mean   2.121568e+05  5.538613e+05  8.770308e+02  2.470941e+03  4.153348e+03   \n",
       "std    1.222872e+05  3.221459e+05  4.486269e+02  1.473310e+03  2.397679e+03   \n",
       "min    1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00   \n",
       "25%    1.063360e+05  2.731680e+05  5.550000e+02  1.151000e+03  2.027000e+03   \n",
       "50%    2.126540e+05  5.555290e+05  8.210000e+02  2.459000e+03  4.065000e+03   \n",
       "75%    3.177500e+05  8.306890e+05  1.252000e+03  3.760000e+03  6.196000e+03   \n",
       "max    4.241700e+05  1.113166e+06  1.671000e+03  4.995000e+03  8.477000e+03   \n",
       "\n",
       "         time_stamp   action_type  \n",
       "count  5.492533e+07  5.492533e+07  \n",
       "mean   9.230953e+02  2.854458e-01  \n",
       "std    1.954305e+02  8.075806e-01  \n",
       "min    5.110000e+02  0.000000e+00  \n",
       "25%    7.300000e+02  0.000000e+00  \n",
       "50%    1.010000e+03  0.000000e+00  \n",
       "75%    1.109000e+03  0.000000e+00  \n",
       "max    1.112000e+03  3.000000e+00  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_log.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 查看正负样本的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "正负样本的数量：\n",
      " label\n",
      "0    244912\n",
      "1     15952\n",
      "Name: user_id, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='label', ylabel='count'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "label_gp = train_data.groupby('label')['user_id'].count()\n",
    "print('正负样本的数量：\\n',label_gp)\n",
    "_,axe = plt.subplots(1,2,figsize=(12,6))\n",
    "train_data.label.value_counts().plot(kind='pie',autopct='%1.1f%%',shadow=True,explode=[0,0.1],ax=axe[0])\n",
    "sns.countplot('label',data=train_data,ax=axe[1],)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上图可以看出，样本的分布不均衡，需要采取一定的措施处理样本不均衡的问题：\n",
    "\n",
    "* 类似欠采样,将一份正样本和多分负样本组合成多分训练集,训练多个模型后求平均\n",
    "* 调整模型的权重"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 探查店铺、用户、性别以及年龄对复购的影响"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 查看不同商家与复购的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "选取top5店铺\n",
      "店铺\t购买次数\n",
      "4044    3379\n",
      "3828    3254\n",
      "4173    2542\n",
      "1102    2483\n",
      "4976    1925\n",
      "Name: merchant_id, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('选取top5店铺\\n店铺\\t购买次数')\n",
    "print(train_data.merchant_id.value_counts().head(5))\n",
    "train_data_merchant = train_data.copy()\n",
    "train_data_merchant['TOP5'] = train_data_merchant['merchant_id'].map(lambda x: 1 if x in [4044,3828,4173,1102,4976] else 0)\n",
    "train_data_merchant = train_data_merchant[train_data_merchant['TOP5']==1]\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.title('Merchant VS Label')\n",
    "ax = sns.countplot('merchant_id',hue='label',data=train_data_merchant)\n",
    "for p in ax.patches:\n",
    "    height = p.get_height()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从图可以看出不同店铺有不同复购率，可能与不同店铺售卖的商品有关，以及店铺的运营有关。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 查看店铺复购概率分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "merchant_repeat_buy = [ rate for rate in train_data.groupby(['merchant_id'])['label'].mean() if rate <= 1 and rate > 0] \n",
    "plt.figure(figsize=(8,4))\n",
    "\n",
    "ax=plt.subplot(1,2,1)\n",
    "sns.distplot(merchant_repeat_buy, fit=stats.norm)\n",
    "ax=plt.subplot(1,2,2)\n",
    "res = stats.probplot(merchant_repeat_buy, plot=plt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出不同店铺有不同复购率，大致在0-0.3之间\n",
    "\n",
    "### 查看用户大于一次复购概率分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OPz1YROYf/4g6moTREL2IiKS2p58OStHecgv07x91NAmjHryIiKSu1avhzDNh1KjgZzuiHryIiKSuSy+FH38MJtZltK+Upx68iIikpqlT4c47g5rz224bdTQJpwQvIiKpp6IiuOe9d2+45pqoo4lE+xqvEBGR9uHOO+GTT+CJJ6Bbt6ijiYR68CIiklrmz4e//hX22w8OPzzqaCKjBC8izWZmD5rZEjP7op79x5rZ52Y23czeM7MtEh2jtENnngmVlXDXXWAWdTSRUYIXkZZ4CNi3gf2zgV3dfTPgKmBcIoKSduz554MZ85ddBoMHRx1NpHQNXkSazd0nm9mgBva/V+Pl+0C/uAcl7VdRUVCxbtNN4dxzo44mckrwIpIoJwIv17fTzMYAYwAGDBiQqJgklVxxBcybB489BpmZUUcTOQ3Ri0jcmdnuBAn+gvrauPs4dy9w94L8/PzEBSepYdq0YLW4P/0Jdtgh6miSgnrwIhJXZrY5cD+wn7svizoeSUGVlcE97z17wvXXRx1N0lCCF5G4MbMBwNPA8e7+XdTxSIq67z744AP497+DJC+AEryItICZPQrsBuSZ2XzgciATwN3vBS4DcoG7LbhdqcLdC6KJVlLSjz/CRRfBXnvBMcdEHU1SUYIXkWZz96Mb2X8ScFKCwpH26OyzobQU7r67Xd/zXhdNshMRkbbplVfg8cfh4oth6NCoo0k6SvAiItL2lJQEq8QNHw4X1HtzRrumIXoREWl7rr4aZs+GN9+EDh2ijiYpxa0HX1eNajO7ycy+CWtTP2Nm3es5dk5Yu/ozM5sarxhFRKQN+vJLuOkm+MMfYLfdoo4macVziP4hflmj+jVgU3ffHPgOuKiB43d391GacSsiIj+pqoJTToGuXeHmm6OOJqnFLcG7+2Rgea1tr7p7RfhSdalFRKRpHnwQ3nknSO55eVFHk9SinGT3f9Rfl9qBV83s47A+tYiItHdLlsBf/gK77AInnBB1NEkvkkl2ZnYJUAGMr6fJTu6+wMx6Aa+Z2TfhiEBd59ICFSIi7cF55wUrxt17r+55j0HCe/BmdgJwAHCsu3tdbdx9QfhzCfAMsG1959MCFSIi7cAbb8C//hX04DfZJOpo2oSEJngz2xf4C3Cgu5fU0ybHzLpUPwf2Ab6oq62IiLQD69bBqafCkCFwySVRR9NmxG2Ivp4a1RcBHQiG3QHed/dTzGxD4H533x/YAHgm3J8B/MfdX4lXnCIikuSuvx5mzIBXX4VOnaKOps2IW4Kvp0b1A/W0XQjsHz6fBWwRr7hERKQN+fZbuO46OPpo2HvvqKNpU1SqVkREkpN7cM97djbcemvU0bQ5KlUrIiLJ6V//gkmTglnzvXtHHU2box68iIgkn2XL4NxzYfvt4U9/ijqaNkkJXkREks8FF8CKFUHvPU2pqjn0qYmISHJ5+2144IGgB7/55lFH02YpwYuISPIoK4OTT4aBA+Gyy6KOpk3TJDsREUkeN98MX38NL74IOTlRR9OmqQcvIiLJ4fvv4aqr4LDD4De/iTqaNk8JXkREoucOp50GmZlw++1RR5MSNEQvIiLRe/zxoBTtHXdA375RR5MS1IMXEZForVwJf/4zFBQEvXhpFerBi4hItC6+GAoLYcIESE+POpqUoR68iIhE5/33g2I2Z54JW20VdTQpRQleRNZjZmlm1jXqOKQdKC8P7nnv2xeuvDLqaFKOEryIYGb/MbOuZpYDfAF8ZWbnRx2XpLjbb4fPPw8m1nXpEnU0KUcJXkQARrr7auBg4GVgMHB8pBFJaps7Fy6/HH77Wzj44KijSUlK8CICkGlmmQQJ/nl3Lwe8sYPM7EEzW2JmX9Sz38zsDjObaWafm5kuskbMLBkezguDTqeoxBjwwp1YmiVBTIl5pKcHPzMygp95ecEjLQ0GDYLx44PHoEHrb2sOzaIXEYD7gDnANGCymQ0EVsdw3EPAncAj9ezfDxgaPkYD94Q/JQJmUUcQOIRn+C0vci43M48BUYeTUFVVwc/KyuDnsmU/75s7F/74x+Dvqazs521jxgTPjz22ae+lHryI4O53uHtfd9/fA3OB3WM4bjKwvIEmBwGPhOd8H+huZn1aKWxpg7qwmjs4k8/Ygts5K+pwkk55+c/JvVpJCVxySdPPpQQvIpjZBmb2gJm9HL4eCfyhFU7dF5hX4/X8cFtdMYwxs6lmNrWwsLAV3lqS0VX8lQ1ZyMncR6UGkWP2ww9NP0YJXkQgGGqfCGwYvv4O+HMiA3D3ce5e4O4F+fn5iXxrSZCt+JjTuZN7OJUPdaWmSQY040qGEryIAOS5+xNAFYC7VwCVrXDeBUD/Gq/7hduknUmjkvs4mSX04mKujTqcpJWZCVlZ62/LzoZrrmn6uZTgRQSg2MxyCWfOm9l2wKpWOO/zwO/D2fTbAavc/cdWOK80gzd6X0T8jOUuCviYP/N3VtMtukAilhZm3eqKvLm5wcMMBg6E//f/4MEHg+fV28aNa/oEO9AsehEJnEOQjIeY2btAPnB4YweZ2aPAbkCemc0HLgcyAdz9XmACsD8wEygB/hiP4CV2kST5+fNhk0tgp315fMLveDxJZvMns+Yk9NqU4EUEd//EzHYFhgMGfBveC9/YcUc3st+Bsa0TpbRZZ50FFRVw113Jc69eO6AELyKY2e9rbdrKzHD3+u5vF4nNiy/C00/DtdfCRhtFHU27ogQvIgDb1HjeEdgT+IT6C9iINK64GMaOhZEj4dxzo46m3VGCFxHc/Yyar82sO/BYNNFIyrjiiuAG7rff/uXUcIk7zaIXkboUEyw4I9I806bBbbfBSSfBTjtFHU27pB68iGBmL/Dz4jJpwEjgiegikjatqgpOOQV69oQbbog6mnZLCV5EAG6u8bwCmOvu86MKRtq4cePg/ffhkUeCJC+RUIIXEdz9rahjkBSxaBFceCHssQccd1zU0bRrSvAi7ZiZraHudd+N4Db2rgkOSdq6s8+GtWvhnnt0z3vEYkrwZvY08ADwsrtXxTckEUkUd+8SdQySQl59FR57LJg9P2xY1NG0e7HOor8bOAaYYWbXm9nwOMYkIhExs15mNqD6EXU80oasXQunnhok9gsvjDoaIcYE7+6vu/uxwFbAHOB1M3vPzP5oZpn1HWdmD5rZEjP7osa2nmb2mpnNCH/2qOfYP4RtZphZa6xLLSL1MLMDzWwGMBt4i+D3/OVIg5K25eqrYdYsuPde6NAh6miEJtwHH640dQJwEvApcDtBwn+tgcMeAvatte1C4A13Hwq8Eb6u/V49CRatGA1sC1xe3xcBEWkVVwHbAd+5+2CCSnbvRxuStBlffQU33QS//z3svnvU0UgopgRvZs8AbwPZwG/d/UB3fzysftW5vuPcfTKwvNbmg4CHw+cPAwfXceivgdfcfbm7ryD4ElH7i4KItJ5yd18GpJlZmru/CRREHZS0AdX3vHfpAjff3Hh7SZhYZ9H/090n1NxgZh3cvdTdm/qfwAY11oNeBGxQR5u+wLwar+eH237BzMYAYwAGDNAlQ5FmWmlmnYHJwHgzW0JQzU6kYQ89FJSivf9+yM+POhqpIdYh+qvr2DalpW8eLiXZotWJ3X2cuxe4e0G+/nGJNImZHWFmHQlG1kqAs4FXgO+B30YZm7QBhYVw/vmw887wxz9GHY3U0mAP3sx6E/ScO5nZlgT3xgJ0JRiub47FZtbH3X80sz7AkjraLAB2q/G6HzCpme8nIvU7BrgLmAg8Ckx094cbPkQkdN55sGZNMLEuTUubJJvGhuh/TTCxrh9wa43ta4CLm/mezwN/AK4Pfz5XR5uJwLU1JtbtA1zUzPcTkXq4+yFm1hU4BDgDeMDMngMeVXU7adCbbwalaC++OFgOVpJOgwk+/Cb/sJkd5u5PNfXkZvYoQU88z8zmE8yMvx54wsxOBOYCvwvbFgCnuPtJ7r7czK4CPgpPdaW7156sJyKtwN1XE0x4fTi8W+Zw4A4z6+nu/aONTpJSaWkwsW6jjeDSS6OORurR2BD9ce7+b2CQmZ1Te7+731rHYTX3H13Prj3raDuV4Ba86tcPAg82dH4RaT3hiNmhwJFAT+DJaCOSpHX99fDddzBxInTqFHU0Uo/Ghuhzwp/13gonIm1XOHP+EOBoYEuCS2hXAZPCSbAi6/vuO7j2WjjqKNhnn6ijkQY0NkR/X/jzb4kJR0QSbA7BrPm7CSbYlUcbjiQ196AcbadOcNttUUcjjYi10M2NZtbVzDLN7A0zKzQzrQMo0vb1d/fj3P1FJXdp1L//Df/7XzBE37t31NFII2K9r2GfcCLOAQTf+DcGzo9XUCKSGO6+NuoYpI1YvhzOPRe22w7GjIk6GolBrJXsqtv9Bvivu68yrfMrItJ+XHBBkORff133vLcRsf4tvWhm3wBbA2+YWT6wLn5hiYhI0njnnaAU7dlnw+abRx2NxCimHry7X2hmNwKr3L3SzIoJSluKSBtmZi/QQLlodz8wgeFIMiorg5NPhgED4Ioroo5GmiDWIXqAEQT3w9c85pFWjkdEEqt6+a9Dgd7Av8PXRwOLI4lIkssttwTLwb7wAuTkNN5ekkZMCd7M/gUMAT4DKsPNjhK8SJtWXY7WzG6ptTLkC2Y2NZZzmNm+wO1AOnC/u19fa/8Agkp53cM2F9ZenVKS1KxZcOWVcOihcMABUUcjTRRrD74AGKnCFyIpK8fMNnL3WQBmNpifC13Vy8zSCRar2ZtgWeePzOx5d/+qRrNLgSfc/R4zGwlMAAa19h9AWpk7nHYaZGTA7bdHHY00Q6wJ/guC4bsfG2soIm3S2cAkM5tFsGrkQODkGI7bFphZ44vBYwTzc2omeCdYgRKgG7CwtYKWOHriiaAU7e23Q79+UUcjzRBrgs8DvjKzD4HS6o2agCOSGtz9FTMbSjDXBuAbdy9t6JhQX2BejdfzgdG12lwBvGpmZxCMCuzVwnAl3lauhD//GbbeGsaOjToaaaZYE/wV8QxCRKJlZtnAOcBAd/+TmQ01s+Hu/mIrnP5o4CF3v8XMtgf+ZWabuntVrRjGAGMABgwY0ApvK812ySWwZAm8+CKkp0cdjTRTTPfBhxNx5gCZ4fOPgE/iGJeIJNb/A8qA7cPXC4CrYzhuAVBzSdl+4baaTgSeAHD3KUBHglHB9bj7OHcvcPeC/Pz8pkUvreeDD+Cee+D004MevLRZsdai/xPB0pH3hZv6As/GKSYRSbwh7n4jUA7g7iUE1+Ib8xEw1MwGm1kWcBTBinQ1/UC4RLSZbUKQ4AtbK3BpRRUVwT3vG24IV10VdTTSQrEO0Y8lmEzzAYC7zzCzXnGLSkQSrczMOhEWvTGzIdSYb1Mfd68ws9OBiQS3wD3o7l+a2ZXAVHd/HjgX+KeZnR2e/wTdkZOkbr8dpk2Dp56Crl0bby9JLdYEX+ruZdX158NiN/oFFUkdlxMsG9vfzMYDOwInxHJgeE/7hFrbLqvx/KvwfJLMfvgBLrssuN/9kEOijkZaQawJ/i0zuxjoZGZ7A6cBL8QvLBFJFDNLA3oQVLPbjmBo/ix3XxppYJI47sE1d4A77wQtJpYSYl1s5kKCa2bTCe6NnUBQvEJE2rhwNvtf3H2Zu78Urg2v5J6i9toryN/Vj732Ap59NihF+7e/wcCBUYcorSTWxWaqzOxZ4Fl31+QYkdTzupmdBzwOFFdvdPfl0YUkrW2vveCNN9bf9sEbayh85wzyN98czjormsAkLhpM8BZcdL8cOJ2wt29mlcA/3P3K+IcnIglyZPizZlUTBzaKIBaJk9rJHeBKLiO3dCHc9yRkZiY+KImbxobozyaYHLONu/d0954EVap2DGfEikgKcPfBdTyU3FPclnzCmdzBvZwC220XdTjSyhpL8McDR7v77OoNYc3p44DfxzMwEUkcM8s2s0vNbFz4eqiZafmwFJZGJfdxMkvoxcVcG3U4EgeNJfjMuibbhNfhNZYjkjqqK9ntEL6OtZKdtCF77vnz89O4m22YytncRsGe3SOLSeKnsQRf1sx9ItK2NLeSnbQhr78eJPkNWcA1XMJE9mHpHkfy+utRRybx0Ngs+i3MbHUd242g3KSIpIZmVbKTtuf114HDz4KXyvn1F3fz6yH6HpeqGkzw7q5lhETah2ZXspM25qWXglK011wDQ4ZEHY3EUayV7EQkhbn7a2b2Capkl9qKi4P13UeOhPPOizoaiTMleJF2zMy2qrXpx/DnADMb4O5aFjqVXHklzJ0LkydDVlbU0UicKcGLtG+3hD87AgXANIIe/ObAVH5eH17aus8/h1tugRNPhJ13jjoaSYBYa9GLSApy993dfXeCnvtW7l7g7lsDWxLcKiepoKoqWOe9Rw+44Yaoo5EEUQ9eRACGu/v06hfu/oWZbRJlQNKK/vlPeP99ePhhyM2NOhpJECV4EQGYbmb3A/8OXx8LfB5hPNJaFi2CCy+E3XeH44+POhpJICV4EYHglrhTgerlxCYD90QWjbSec86BkhK45x4wq3Opd/fEhyXxl/AEb2bDCZakrLYRcJm7/71Gm92A54DqGvhPa/U6kfgws3Tg5fBa/G1RxyOt6NVX4dFH4fLLYfjwOpM7BOvCK8mnnoQneHf/FhgFP/3HsgB4po6mb7u7FrsQiTN3rzSzKjPr5u6roo5HWsnatXDaaTB0aDBEL+1O1EP0ewLfu/vciOMQae+KCK7DvwYUV2909zOjC0la5Npr4fvvg0XgO6qyeHsUdYI/Cni0nn3bm9k0YCFwnrt/mbiwRNqdp8OHpIKvvw5uhzv+eNhjj6ijkYhEluDNLAs4ELiojt2fAAPdvcjM9geeBYbWc54xwBiAAQMGxCdYkTiqqKrioXdnc8AWG5LXuUNUYTwObBw+n+nu66IKRFrIHU45BTp3hptvjjoaiVCUhW72Az5x98W1d7j7ancvCp9PADLNLK+uk7j7uLA4R0F+fn58IxZpZe7Of6fO54oXvuL6l79J+PubWYaZ3QjMBx4GHgHmmdmNZpaZ8ICk5R56KChFe+ON0KvXervqm0inCXapKcoEfzT1DM+bWW+zYL6nmW1LEOeyBMYmkhArS8qZviCY1/bi5wtZWVKW6BBuAnoCg919a3ffChgCdAfU/WtrCguDRWR23BH+7//qbOL+y4ekpkgSvJnlAHtT45qfmZ1iZqeELw8Hvgivwd8BHOWuf4aSeuYuLwHg+kM3Y115FW9+uyTRIRwA/Mnd11RvcPfVBPfE75/oYKSFzj8fVq+G++6DNFUib+8i+Rfg7sXunlvzlhx3v9fd7w2f3+nuv3L3Ldx9O3d/L4o4ReJt7rJisjLSOGSrvmRlpPH1j2saP6h1eV1fnt29EtCX6rZk0qSgFO3558OvfhV1NJIE9BVPJEI/LC9hQI9sOmSkM2yDznz94+pEh/CVmf2+9kYzOw5I/KQAaZ7S0mBi3eDBcOmlUUcjSSLq2+RE2q3KKmfx6nXsPDSYHDqid1fe+q4w0WGMBZ42s/8DPg63FQCdgEMSHYw00w03wLffwssvQ3Z21NFIklAPXiQiK0vKqHLI65wFwIjeXShcU8rSotKExeDuC9x9NHAlMCd8XOnu27p7TMvFmtm+Zvatmc00szpLppnZ78zsKzP70sz+01rxCzBjRlDU5sgjYd99o45GkogSvEhElhcHM+Z75FQn+K4AfLc44dfhcff/ufs/wscbsR4Xlpu+i+C215HA0WY2slaboQT1LnZ0918Bf269yNs5dzj1VOjQAW6LbRmB006DjIyg/nxGRvBaUpMSvEhEloe3xOXmBMVt+vfsBMCCFWsji6kZtiUojDPL3cuAx4CDarX5E3CXu68AcPeE3yqQsv7zn6AU7XXXQZ8+jTY/7bRgUbnKyuB1ZWXwWkk+NSnBi0RkeVEZGWlGl47BVJje3TpiBgtWtqkE3xeYV+P1/HBbTcOAYWb2rpm9b2YaR24Ny5fD2WfD6NFw8skxHTJuXNO2S9umSXYiEVleUkaP7CzSwjU8O2Skk9+5AwvbVoKPRQZBqendgH7AZDPbzN1X1mykstNNdOGFQZJ/7TVIT4/pkOqee6zbpW1TD14kIsuLy+gZXn+v1rdHp7bWg18A9K/xul+4rab5wPPuXu7us4HvqGNtCZWdboJ334V//hP+/GfYYouYD6vve0CM3w+kjVGCF4nIypJyumevX+59w+6dWLiyTa3z8hEw1MwGhwtIHQU8X6vNswS9d8I1JYYBsxIYY2opLw/uee/fH664okmHjhnTtO3StinBi0SgvLKKteWVdOu0foLv2z3owVdVtY0icu5eAZwOTAS+Bp5w9y/N7EozOzBsNhFYZmZfAW8C57u71pZorltugS++gLvuClaMa4K77w4m3Vf32NPTg9d33x2HOCVyugYvEoE16yoA6NLxlwm+rKKKZcVl5HeJbOnYJglXfJxQa9tlNZ47cE74kJaYPRuuvBIOOQR++9tmneLuu5XQ2wv14EUisHptOQBdO67/HbtPt44A/LiqTV2Hl0Rwh7Fjg273HXdEHY20AerBi0Rg9bogwXepNUTfq2uQ4AvXJK6anbQRTz4ZlKK97Tbo1y/qaKQNUA9eJALVQ/S1e/DVw/JK8LKeVavgzDNhq63g9NOjjkbaCPXgRSKwel05GWlGp8z170+qrkuvBC/rueQSWLIEXnghqC8rEgP14EUisGZdBV06ZmBhkZtqHTLS6dYpk8IELjgjSe7DD4NZcWPHQkFB1NFIG6IELxKB1evK6VprBn21/C4d1IOXQEVFUIa2Tx+4+uqoo5E2RmM9IhEoWldR721w+Z2V4CV0xx3w2WfBBLuuXaOORtoY9eBFIlBcWkFOh7q/X+d36aAheoEffoDLLoPf/AYOPTTqaKQNUoIXSbAqd0rKKsnJqrsAuIboBQhmzVdVwZ13Bou3izSRErxIgq0rr8SB7Kz6e/AlZZUUl1YkNjBJHs8+C889F9SaHzQo4mCkrdI1eJEEKykN1ubM6VB3Dz6v88/3wtc3jC8pbM0aOOMM2GwzOPvshHTevW0sfSBNpB68SIIVlwU98/p68D1zgtn1K0rKEhaTJJHLL4cFC+C++7Csuu+0aG26ApCalOBFEqz4px58fQk+6MErwbdDn34Kt98erN+6/fZRRyNtnBK8SIKVhD34+ibZ9cwOqtktLy5PWEySBCorg8Senw/XXRd1NJICdIFPJMGKy4IefH1D9D3CIfrlxZpJ367ccw9MnQr/+Q/06BF1NJIC1IMXSbCS0goy042sjLp//Tp3yCAz3dSDb08WLoSLL4a994ajjoo6GkkRSvAiCVZcVllv7x3AzOiZk8WKYl2Dbzf+/GcoKwtqzteY8Zao2e2aRZ+aNEQvkmAlZRX1Xn+v1iM7i+WaZNc+TJgA//1vUGt+441/sVvJV5pLPXiRBGuoTG21njlZLFcPPvWVlASrxG2yCZx/ftTRSIpRD14kwYrLKumZk9Vgmx45WXy9cHWCIpLIXHklzJkDb70FWQ3/mxBpKiV4aRX/+eCHOrcfM3pAgiNJfiVlFWQ30oPPzdEQfcqbPh1uuQX++EfYZZeoo5EUpCF6kQSqrHLWlVfFdA1+1dpyKiqrEhSZJFRVVbDOe/fucNNNUUcjKUo9eJEEKmmkTG21njlZuMOqteXkdq573Xhpw+6/H6ZMgYcegtzcqKORFKUevEgCNVamtlqPnOpqdhqmTzmLF8MFF8Buu8Hvfx91NJLCIkvwZjbHzKab2WdmNrWO/WZmd5jZTDP73My2iiJOkdZU3EiZ2mq5SvCp69xzobg4qFynVV4kjqIeot/d3ZfWs28/YGj4GA3cE/4UabNKqsvUNtaDD+vRa8GZFPPaazB+PFx2GYwYEXU0kuKSeYj+IOARD7wPdDezPlEHJdISxaWx9eB75mjBmZSzbh2cdhoMHQoXXRR1NNIORJngHXjVzD42szF17O8LzKvxen64TaTNinWSXQ+tCZ96rr0WZs4MytF27Bh1NNIORJngd3L3rQiG4seaWbNuBDWzMWY21cymFhYWtm6EIq2suLSSjplppKc1fO21Q0Y6nTtksKwo+RO8me1rZt+G82UubKDdYWbmZlaQyPiSwjffwPXXw7HHwl57NenQrKzgUn31Q/VwJFaRJXh3XxD+XAI8A2xbq8kCoH+N1/3CbbXPM87dC9y9ID8/P17hirSK4rKKRnvv1XrkZCZ9D97M0oG7CL6ojwSONrORdbTrApwFfJDYCJOAO5xyCnTuDLfe2qRDs7KgvNZVmvJyJXmJTSQJ3sxywl94zCwH2Af4olaz54Hfh7PptwNWufuPCQ5VpFWVlFU2ev29Ws/sNlGPfltgprvPcvcy4DGC+TO1XQXcAKxLZHBJ4eGHg1K0N9wAvXo16dDayb2x7SI1RdWD3wB4x8ymAR8CL7n7K2Z2ipmdEraZAMwCZgL/BE6LJlSR1lMSw0Iz1XrmZCV9D54Y5sqEt7j2d/eXGjpRSl5uW7oUzjsPdtwRTjwx6miknYnkNjl3nwVsUcf2e2s8d2BsIuMSibfiskp6d+sUU9seOVl8t7gozhHFl5mlAbcCJzTW1t3HAeMACgoKUmOR1PPPh1Wr4N57IS2Zb1qSVKR/cSIJFMta8NXayBB9Y3NlugCbApPMbA6wHfB8u5ho99ZbQSna886DTTdt1ikyM5u2XaQmJXiRBCkpq6C80hstclOtR04Wa8srWVdeGefIWuQjYKiZDTazLOAogvkzALj7KnfPc/dB7j4IeB840N1/Ub0ypZSWBhPrBg+Gv/612acpK/tlMs/MDLaLNCbqSnYi7UZ1bzzmHnxY7GZlSTm9u8V2TKK5e4WZnQ5MBNKBB939SzO7Epjq7s83fIYUddNNwa1xEyZAdnaLTqVkLs2lBC+SICvCqnSxTrLrkR103ZYXl9G7W/IWRnH3CQSTYmtuu6yetrslIqZIzZgBV18Nv/sd7Ldf1NFIO6YhepEEWR7OiM+OsQevevRtkHtQjrZDB/j736OORto59eBFEmTFT0P0sd8mB1pRrk159FF4/XW4807oo6UzJFrqwYskyLIwUWd3iK0H3z27+hq8EnybsGIFnH02bLNNMMFOJGLqwYskyIriMgzomBlrgq++Bq+yZW3ChRfCsmUwcSKkJ+ekSGlflOBFEmR5SRnZWemkWcMLzVTLTE+ja8cMXYNvC957D8aNg3POgVGj6mySng5VVa3zdp4aZYAkzjREL5IgK4rLYp5BX61H2yhX276Vl8PJJ0P//vC3v9XZpDWTOwSryok0Rj14kQRZXlwW80py1Xq0jWp27dttt8EXX8CzzwYrxtWhNZO7SKzUgxdJkBUlZeTEOMGuWhtZcKb9mj0brrgCDj4YDqprET2R6CjBiyRIc3vwKzTJLjm5w+mnB+Pvd9wRdTQiv6AEL5IAVVXOipLymMvUVuuRnakefLJ66qmgFO2VVwbX3xugheQkCvpnJ5IAa9ZVUFkV+0Iz1XrkZFFSlvQLzrQ/q1bBmWcGM+bPOKPR5pWVrZvkNYteYqFJdiIJUF2mtqk9+OpqditKyugT4zrykgCXXgqLFsFzz0FGbP+NVuo7miSYevAiCfDTSnJN7cHXWHBGksRHH8Fdd8HYsUHVuhiNHw+DBgU9+UGDgtci8aQevEgCVCfoWBeaqdYj++clYyUJVFQE97z37h2sGBej8eNhzBgoKQlez50bvAY49tg4xCmCevAiCdHUhWaqacGZJHPnnfDpp3D77dCtW8yHXXLJz8m9WklJsF0kXpTgRRLgp6Vim3gffHctGZs85s0Lrr3vvz8cfniTDv3hh6ZtF2kNSvAiCbC8uIysjDSy0pv2K9dd1+CTx5lnBiXp7rqrybViBwxo2naR1qAEL5IAS4tKye/cAWtiYqhecEbX4CP23HNBKdrLLw9myDXRNddAdvb627Kzg+0i8aIEL5IAy4rKyO2c1axje+aoHn2kioqCe9033TRYLa4Zjj02WGxu4MCg8z9wYPBaE+wknjSLXiQBlhUHPfjm6J6tevSRuvzy4Pr7Y49BZuZ6u5q7qtvcuXDccUrwEl/qwYskQNCDb16C75mTxbIiJfhIfPZZMGN+zBjYYYf1drXGkq1a9lXiSQleJM7cvUVD9Lk5WSwrLm3lqKRRlZXBPe+5uXD99VFHI9JkGqIXibM1pRWUVVaRl9O8Hnxelw4sKyqjqspJS1OXL2HuvRc+/DCoUtOjR9TRiDSZevAicVY9vN7cHnxe5w5UVDmr1momfcIsXAgXXwx77QVHHx11NCLNogQvEmfLioLh9eZeg88LvxhomD6Bzj4bSkvhnnt0oVzaLCV4kThbWt2Dz2leD7569n3hGk20S4iXX4YnngjqyG68cb3N/v3vlr+Vln2VeFKCF4mz6p53XnN78F2C45YWqQcfdyUlcNppMGIE/OUvDTatr478wIFB4o7lIRJPmmQnEmfV1+B7NrMHX/3FQAk+Aa66CubMgUmToEPDX8hUX16SnXrwInG2rKiUrh0zyMpo3q9b906ZpKdZ0iZ4M9vXzL41s5lmdmEd+88xs6/M7HMze8PMBkYRZ6O++AJuvhlOOAF23RWAX/0quARf16O+Hrjqy0uyUIIXibOlxWXNHp4HSEszcnOyWJqE1+DNLB24C9gPGAkcbWYjazX7FChw982BJ4EbExtlDKqq4JRTgiVgb7oJCJL7V181/VSqLy/JQgleJM6WFZW2KMFDMAM/SXvw2wIz3X2Wu5cBjwEH1Wzg7m+6e/Vq6O8D/RIcY+MeeADefTfoweflAc1L7qDys5I8Ep7gzay/mb0ZDtl9aWZn1dFmNzNbZWafhY/LEh2nSGtpSRW7anmds5I1wfcF5tV4PT/cVp8TgZfr2mFmY8xsqplNLSwsbMUQG7FkCVxwQTAs/4c/JO59ReIsikl2FcC57v6JmXUBPjaz19y99vflt939gAjiE2lVy4rLGN3CBJ/fuQOzCotbKaJomNlxQAGwa1373X0cMA6goKAgcXPMzz03WDHu3nt1z7uklIT34N39R3f/JHy+Bviahr/xi7RZFZVVrCgpI7eZZWqr5XXpQGFRKZ5891YtAPrXeN0v3LYeM9sLuAQ40N2TZyjijTeCG9ovuCC4Na6GkbVnEoi0MZFegzezQcCWwAd17N7ezKaZ2ctm9qsGzhHNsJ5IDFaUlOP+czW65srrnEVZRRVrSitaKbJW8xEw1MwGm1kWcBTwfM0GZrYlcB9Bcl8SQYy/cNpp0NHW8d1epzKDjel09cW/mCXfnGvwyff9S9qzyBK8mXUGngL+7O6ra+3+BBjo7lsA/wCere887j7O3QvcvSA/Pz9u8Yo0R3WRm+aWqa32073wa5Kn8wvg7hXA6cBEgtG4J9z9SzO70swODJvdBHQG/hvOqXm+ntMlxGmnBRVoL+I6hjGDU7mHdXSK+fhTT1XhGmkbIil0Y2aZBMl9vLs/XXt/zYTv7hPM7G4zy3P3pYmMU6SllrWwTG21n4vdlLFRkn2PdfcJwIRa2y6r8XyvhAfVgHHjYBjfciHXM55jeIOmhTduHNx9d5yCE2lFUcyiN+AB4Gt3v7WeNr3DdpjZtgRxLktclNJUq9aWM3NJEaUVlVGHklSW/rTQTGsl+OTqwbdFlZXOvZxCCdmcQ53/BTVyfByCEomDKHrwOwLHA9PN7LNw28XAAAB3vxc4HDjVzCqAtcBRnoSziyTw6Q8r+Pvr31FaUUWvLh34084bkdNBVZABlqwOEnKvrh1bdJ68LsEXBCX4ljvBHmF3n8QY7mMJGzT5+PT0OAQlEgcJ/1/Y3d8BGrwXxd3vBO5MTETSEmUVVZzx6KfkdMjggM178dxnC3jx84UcuY3qdQIsWr2OTpnpdGnhF56e2VmYJd81+DZn2TJuyziPd8t34H5OatYpxoxp5ZhE4kSV7KRF/vvxPOavWMtvN9+QrQf2YIchuXw+fxVLVq+LOrSksHj1Onp364i18P7qjPQ0emZnsbQ4+crVtiUzD/sLOeUrOYV78Wb893fqqbr+Lm2HErw0m7vz8Htz2KJfN4Zt0BmAnYbmk55mvD9bUyYgSPC9urRsBn21/C4dfhryl2aYPJmN33qQWziXL9hsvV2xLvGq5C5tiRK8NNtXP67mu8VFHFHQ/6ceaucOGYzo05XP56+iskrTJhavLqV3t5Zdf6/Wu1tHFq1e2yrnanfKyuCUU5jNIK7kl5WvtcSrpCIleGm25z5bSGa68ZvN+qy3fcv+3Skpq2TmkjURRZYc3J1Fq9exQQsn2FXr060ji1bp0kez3HQTfP01f+t1F2vJ/sVuLfEqqUgJXprt9a8Ws/2QPHrUusd7aK/OZKWn8c2i9p3gV5aUU1ZR1YoJvhNLi8p0K2JTzZwJV18Nhx/O3rfuT3at/J6drSVeJTUpwUuzzF5azKylxew5otcv9mWkpzE4L4cZS4oiiCx5LF4T9LY36No61+Crh/oXr9J1+Ji5B6XrMjPh9tsB6FSjaF1ublC4Rku8SipSgpdm+d83QUnxPepI8ABDN+jM8uIy5i5r2yugtUT1cHrvVhyiB/hxla7Dx+yxx+C11+Daaxn/5oaMGQPLasz/XKuPUlKYErw0y7szlzI4L4f+PX95PRNgWK8uAEye0X6rCy9YGWSPDbvHXue8IX26BedZpFsQY7NyJZx9NhQUwKmnctZZUFKyfpOSErjkkkiiE4k7JXhpsorKKj6cvZzth+TW2ya3cxY9sjOZ/F37XeFv/oq1ZKRZq12Drx6iX7hSCT4W3x1+EZWLC9lq6n1YRvp6PfeaNINeUpUSvDTZFwtXU1RawfYb1Z/gzYyNe3VhyvfLKK+sSmB0yWPBirX06d6R9LSWFbmp1rlDBl06ZmiIPgYTr5jCsDfu5XbO4lO2arCtZtBLqlLBcGmy974Pht23ayDBQzCb/qM5y/n0h5VsO7hnIkKLSVVVFQsXLmT27NksWrSIxYsXs2jRIpYvX05xcTHFxcWUlJRQWVlJWlraT48uXbrQtWtXunXrRs+ePdlwww3p27cv/fr1o3///nTqtP5Q/PwVJfTrXvcljObq3yOb+SuU4Bvyn4fL2fRvJzOPflzGlY221wx6SVVK8NJkU75fxrANOpPfSIW2IfmdMQvaR5XgKyoqmDFjBp999hmff/453333HbNmzWLdup+Huc2MvLw8cnNzycnJITc3l/79+5ORkUFVVRVVVVVUVFRQVFTEkiVLmDFjBsuXL//FOQYOHMjw4cMZNmwYW2yxBfMLV7LryH6t+ucZ0DObmYXt++6EhowfD1/96e8cw3QO5hmK6dxg+9xczaCX1KUEn0L+80HdFxOPGd16Y5BlFVVMnbOC3xU0nrg6ZaUzsk9XpsxaylkMbbUYGlJZWcn06dN59913+eijj5g+fTol4cyq3NxcRowYwe9+9zuGDBnCRhttxIYbbkh+fj6ZmZlNeh93Z/ny5SxYsID58+cze/ZsvvnmG77++msmTpwYtLE03u83mGtn7EBBQQHbb799i/98A3KzefPbJVRVOWmtNPSfCsaPDyfLzZ3Dl1zBcxzIcxzc4DFmP905J5KSlOClST6fv5K15ZVsPyQvpvbbb5TLI+/PZV15JR0z47PO5vLly5k0aRJvv/02U6ZMYdWqVZgZI0eO5JBDDmHUqFGMGjWKvn37tnjRl2pmRm5uLrm5uWy++ebr7SsqKmLi5A+44L5n6ZJeyBNPPMG//vUvMjIy6Df0V2y8+TZsvPm25G04oMnx9O+ZTWlFFYVFpa02ea+tGz8+WOGtpMR5gdNxjDP4R6PHnXKKeu+S2pTgpUne+34ZZrDdRrENuW8/JJf735nNJz+sYIcYvxTEorCwkNdff51XX32VDz/8kKqqKnr16sWee+7JTjvtxPbbb0/37t1b7f2aonPnzuRtvDkVm6zj8jHbsWW/LkybNo3Jkyfz/Ctv8Prj9/P64/fTc4MN2WSbXRi57S74tv1jSvYDwtsSf1he0m4TfHVv/YcfgglyRUXB7W6H8jQH8BLncAvzqH/UKjc36LkruUuqU4KXJpny/TI26d2V7tlZjTcGth3ck/Q0Y8r3y1qc4FetWsVLL73Eyy+/zMcff4y7s9FGG3HyySez9957M2LEiFbrobfUrPA6+eD8HLKysthmm23YZptt6LPTEaxatoSZ0z7k64/f4b2XnuDdFx/j9f83mH333Zf99tuPoUPrv5zxU4JfVsI2g5Jn4mKi/NxbD17PnRv87MJq7uBMPmUUd3DmesdkZ6tanbRPSvASs3XllXz8wwqO325gzMd06ZjJpn27MeX75i0fW1VVxUcffcSTTz7Ja6+9RmlpKRtvvDFjx45ln332aTAZRmlWYTFdOmSQ3/mXExG75fZi6z0OYOs9DqB49Uq++fhdln3zAffddx/33HPPT5cWfvOb39CjR4/1ju3bvRNpRrutEHjJJb8sVgNwFX+lDz9yCM9QWeO/tYEDg1nySu7SHinBS8w++WEFZRVV7NBAgZu6bL9RLg+8M4uSsgqys2L7J7d48WKeeeYZnn76aebNm0fXrl057LDDOOywwxg5cmRzwk+o2UuL2Sg/p9ERhZyu3dl6999wzIWnUlhYyCuvvMKzzz7LNddcw4033shuu+3GIYccws4770xGRgZZGWkM6JnN94XtM8HXVZRma6ZyBv/gbk7jI7YF1GsXASV4aYK3ZywlI82afMvb9kNyufet75k6ZwW7DMuvt115eTmTJk3iySef5J133qGqqorRo0dzxhlnsPfee9OxY9u55jyrsKjJn1N+fj7HH388xx9/PN9++y3PPPMML7zwAq+99hp5eXkceOCBHH744Wzcqwsz2ulSvAMG/DwsD5BOBfdxMkusN/f1uwabH7RRr11ECV6a4K1vC9lqYA+6dGzaLWUFA3uQkWZMmbWszgQ/a9YsnnrqKZ577jmWLVvGBhtswJ/+9CcOPfRQBrTBMmMlZRUsXLWOjfIbvge7IcOHD+fCCy/knHPO4e233+aZZ57h4Ycf5sEHH6T3xpuyoMcWFJVsS+fs1qlz31Zcc8361+DHchdb8wlvj32c6f/oFm1wIklGCV5iUrimlK9+XM35vx7e5GNzOmSwRf/u612HLy4u5pVXXuGpp57i008/JSMjg913353DDjuMnXbaifT0+NxSlwizwuHzIS1I8NWysrLYc8892XPPPVmyZEmQ6Mc/RvrM8eyxx4scevDBHHHEEQwZMqTF79UWVPfKL7kEKufO5xq7lIWb7cvOdxwRbWAiSUgJXmLy9oxg0ZhdGxhib8gOQ3K5+82ZvPvBVF558TkmTJhASUkJG220Eeeffz4HHnggeXmtdxtdlL5cuAqAkRt2bdXz9urVi5NPPpnt9vsdh1zxEFv4t4wfP56HH36YrbfemiOOOIJf//rXbepSRnMce2yY6A89E16ppPOzdwdVa0RkPUrwEpO3viskr3MWI/s0PWktX76ctV++QcYbT3DSs4vJzs5m33335fDDD2fUqFFJc2tba/ly4Wo6d8hgYD1L6bbU0N5dYINhbLvHb7jzpqt59tln+e9//8uFF17Iddddx4QJE+jZM8VvoXvhBXjmGbjuOhg8OOpoRJKSErw0qqrKeXvGUnYdlh9zedSqykpmTv+IM8ffzKRJkygvLycjbxDDdj2Jf19xCjk5OXGOOjpfLlzNJn26xK2UbHZWBkN7deHz+SvJ23sYJ510EieeeCIffvghH374Yeond4CKCth9dzj33KgjEUlaSvDSqGnzV7K8uCym4fnChT8w7e1Xmf7eGxStWk7Pnj059thjOfTQQ3ng87W8+PmPZHRI3SHkyirnq4WrOXKb/nF9n1H9u/PqV4twd8wMM2P06NGMHj06ru+bNA45BA4+WEPzIg1Qgk9hFVVVFK2roKi0gs4dmv9X/dLnP5KVnsbuI3rVuX/16tV8/OZLTHv7VRbM+oa09HQ23nxbRu28D5eedNhPC7nsXbGYxz6ax/uzljf7Wn6y+27xGtaWV7Jp3/jO6N6if3cenzqPecvXMiA3PpcCYmVm+wK3A+nA/e5+fa39HYBHgK2BZcCR7j6nFd64xacQSWVK8CloeXEZb3y9mC8WrqK80rnp1W/ZYUgu5+0znC0H9Gj8BDVUVTkTpv/IzkPz6Nbp59vjSkpKmDRpEhMmTGDy5MmUl5eT328Qex81hs122JOcrt0B1lulbceN8+iUmc7rXy1O2QT//qzgToHRcV4ed4v+wReIT35YEWmCN7N04C5gb2A+8JGZPe/uX9VodiKwwt03NrOjgBuAIxMfrUj7ogSfYqbNW8mzny2gyp0t+/egb49O9OvRicc+msdh97zHxftvwkk7bxTz+d6ftYyFq9bxl31HUFZWxttvv82ECRN48803Wbt2Lb169eKYY44hY1ABfQYNbXDCXMfMdHYZlsfELxdx+W9HkpGe1hp/5KTywazl9OvRif5xmmBXbUTvrnTrlMk7M5dy8JZ94/pejdgWmOnuswDM7DHgIKBmgj8IuCJ8/iRwp5mZu3siAxVpb5TgU0RxaQVPfTyfj39YwYCe2Ry5TX96hAvCHDN6AH/aZSMuePJzrn7pa1aUlHHePsNjmr3+8ORv6LxkOm/9639c+87brFmzhh49enDggQey//77U1BQQFpaWr1r0dd2yJZ9mfjlYibPKGSPERu06M+cbCqrnA9mL2PPTeL/50pPM3YamsfbMwp/ug4fkb7AvBqv5wO1JwL81MbdK8xsFZALLK3ZyMzGAGOANlngSCTZKMGngC8XruKMRz9ldmExuw3PZ88RG5BeawZ3146Z3HnMVlz67Bfc9eb3rCuv4tLfbFJnYliwYEEw/D7xNT6e+hHmVXzYsyd77703++23H6NHj15v6L0p9hixAbk5WTz64byUS/BT5yxnRUl5wi4/7Do0n5c+/5FvF69hRO/Wvec+Cu4+DhgHUFBQoN69SAspwSephnrEx4wOejdryyq5Z9JM7n1rFt2zM/m/nQY3WD0tPc249pBN6ZCRxgPvzCYrI42//Ho4q1ev5oMPPuC9995jypQp/BCu6NE5vy8+dFfuOPf37LnjNq1SXS4rI40jt+nPPW99z6zCohaVc002L03/kY6ZaexRz2TE1rbb8HzSDF6c9mOUCX4BUPOWgX7htrrazDezDKAbwWQ7EYkjJfg2aHlxGU9Mnccj781h4ap1HDRqQy47YCQTv1zc6LFmxmnb5TN72hT+eecLvHTbQpbN/56qqiqys7MZPXo0xx13HHkbb8HpL8zn2NED2GeXTVs1/j/uOJgH3pnN3ZO+5+YjtmjVc0eltKKSCdMXsfvwXuS04I6FpujVtSO7DMvnqU/mc/bew34xapMgHwFDzWwwQSI/CjimVpvngT8AU4DDgf/p+rtI/CnBtwFV7ixcuZbvFhfx3eI1XPrsdKocttuoJ7ceOYrtNqp/+daydWv59NNP+eqrr5g2bRqfffYZ8+YFl0w7ZGSyqGtfNt39UM4//kAKthpFZmYmS1av43f3TaFXlw6cu0/Ta883Jr9LB47fbiAPvDub328/kM37dW/190i05z5dyNKiUo7eNrHXjo/Yuj9j//MJr3+9mF//qndC3xt+uqZ+OjCR4Da5B939SzO7Epjq7s8DDwD/MrOZwHKCLwEiEmdK8EnI3VlWVMrMwiJmLiliVmExa8srAejbvRNjd9+YAzbfkOG9u/x0THl5OcsWzWfZj/MpXDiXRXO/Z9EP37N88QIIO0t5eXlsueWWHH300YwaNYpNNhnJbf+bxX2TZ3HR5CKOKAnW4XxkyhyK1lXwyImj17s1rjWduddQnpu2kPP+O41nx+4Y8zrxyWhdeSV3T5rJrzbsys5DE1tP/9e/2oCN8nK45dVv2WuTX869SAR3nwBMqLXtshrP1wFaDUYkwSL5XzWywhhJbNXacqZ8v5TJM5by9oxC5i1fC0C3TpmM3LArG+d3pl+XNCqLlrNVx8V89tZnPDd3LnPmzGH27NnMnz+fioqKn87XLXcDeg8cwqbb7c6Re2/HiBEj6NOnzy8m1V20/ybsuHEet7z6LTdN/BYIqqRd/YdN41qspWvHTG793Rb8/sEPGTv+E+45bms6ZrbNFeRunvgtc5aV8K8Tt034bPaM9DTO//VwTh3/CXe8MYOz9x6W0PcXkeSV8ASvwhhQUVnFrKXFTJu3kk9nL+az7xfy7ZyFeGkRHavWMrBzFSPWFdOpqoTy4tUUvlPI98uWULq2ZL3zZGVlMXDgQIYNG8Y+++zDoqqu5PbuR26ffnTK+bl3v8fohoeNdxmWzy7D8lm1thx3p3t4e1287Tw0n2sO3oxLnp3OwXe9yyW/2YQdh+TVW8O9vomHxzTy54uXyirnH/+bwf3vzOb47Qay89Boivfsu2lvDt2qL7e/MYOunTI5cSctviIi0fTgIyuMUVZWRmVlJVVVVVRWVv70qP26srKSiopKyisqqKyqoqKygsqKKiorK6gI95WWllJaVsa6daWUrF1Hybp1lKxdx9p1peFjHcUl61hTspaiNUUUFRVRVFzEupJiykvXQvk6KF+HEfyRqlOqA3OAtPQMcrp2J6drd3rk92HQ8M3pmtuLrj3zOWKXzejduze9evVab2Z7rPei1ydew/ENOWb0APp068hFT0/n+Ac+pHt2JgNzc8jNyaK8sorS8ipKyisoWldBYVEZpeWVuENaGqSZkZ2VzuMf/UBu5w7k5mTRs3MWeTkd6JmTRW7nLHJzOtC5YwZZGWlkpafRITP4mZWe1qTFYCqrnHXllaxZV8HcZcVMX7CKJz+ezzeL1nDIln25/Lcj4/gpNczMuPaQzSgpreTWV7/lN5v1oXe31K33LyKxiSLBt1phjKbaYqttoLKsJaeImadnQloGpGXimR2wzI5kdsymQ/cN6J6dQ+fOnenRrQs9unUlu3MXsrt2J6dLN7K7dCO7S3c6dMqud7h3yy1TqwjI7iN68dZfduOVLxbx3sxlLFy1lkWr1pGVkUanzHR6denIkPwMfly1jg4ZaaSZUeVOZZWztqySrp0yWbJmHV//uJplRWWUVVY1OYbqj9rgp8+95qdfUfXL75ab9OnKHUdvyW83/+Wlj0TrmJnOXcduxfeFRUruIgKkwCS7mtWvgCIz+7aFp8yjhV8k4izv2CbGd2y8IvmlX3x2CXzvWLTq3+1c4JVWOtexif13NzBB79NsH3/88VIzm1vP7mT/HW1IW44d2nb8qRp7vb/PUST4Vi2MUbP6VWsws6nuXtBa52ttyRxfMscGyR1fMscWBXevd0JDW/6s2nLs0Lbjb4+xR7Hax0+FMcwsi+Ce2OdrtakujAEqjCEiItJkCe/BqzCGiIhI/EVyDT7JC2O02nB/nCRzfMkcGyR3fMkcW7Jpy59VW44d2nb87S5208i3iIhI6oniGryIiIjEWbtK8Ga2r5l9a2YzzezCOvZ3MLPHw/0fmNmgGvsuCrd/a2a/jiC2c8zsKzP73MzeMLOBNfZVmtln4aP2hMVExXeCmRXWiOOkGvv+YGYzwscfah+bgNhuqxHXd2a2ssa+RHx2D5rZEjP7op79ZmZ3hPF/bmZb1dgX18+urTOzc83MzSyxiwC0gJndZGbfhH/Xz5hZ96hjakxjv2PJysz6m9mb4f+dX5rZWVHH1FRmlm5mn5rZi00+2N3bxYNgQt/3wEYEheOmASNrtTkNuDd8fhTwePh8ZNi+AzA4PE96gmPbHcgOn59aHVv4uigJPrsTgDvrOLYnMCv82SN83iORsdVqfwbBxM6EfHbhe+wCbAV8Uc/+/YGXCWrrbAd8kIjPrq0/CG6lnUhQkiAv6niaEPc+QEb4/AbghqhjaiTeJv2OJdMD6ANsFT7vAnzXVmKv8Wc4B/gP8GJTj21PPfifSuS6exlQXSK3poOAh8PnTwJ7WlCi7CDgMXcvdffZwMzwfAmLzd3fdPfqYvTvE9QPSJRYPrv6/Bp4zd2Xu/sK4DVg3whjOxp4tBXfv1HuPpngbpD6HAQ84oH3ge5m1of4f3Zt3W3AX4A2NZHI3V919+qVoRL9u9wcLfn9j5S7/+jun4TP1wBfE1RKbRPMrB/wG+D+5hzfnhJ8XSVya/9Fr1ciF6gukRvLsfGOraYTCXp81Tqa2VQze9/MDm7FuJoa32HhsOOTZlZdzChpPrvwssZg4H81Nsf7s4tFfX+GeH92bZaZHQQscPdpUcfSQv/H+r/LySgl/h2Gl1y3BD6IOJSm+DvBl9im198mBUrVtjdmdhxQAOxaY/NAd19gZhsB/zOz6e7+fYJDewF41N1LzexkgpGQPRIcQ2OOAp5098oa25Lhs5M6mNnrQO86dl0CXEww1J2UGord3Z8L21wCVADjExlbe2RmnYGngD+7++qo44mFmR0ALHH3j81st+acoz0l+JaUyI3l2HjHhpntRfCf267uXlq93d0XhD9nmdkkgm+prZmkGo3P3WuWEr4fuLHGsbvVOnZSImOr4ShgbM0NCfjsYlHfnyHen11Sc/e96tpuZpsRjMRMC66g0Q/4xMy2dfdFCQyxXvXFXs3MTgAOAPb08EJrEov3/39xZWaZBMl9vLs/HXU8TbAjcKCZ7Q90BLqa2b/d/biYzxD1BIIETlTIIJikNJifJ4r8qlabsaw/ye6J8PmvWH+S3Sxad5JdLLFVJ56htbb3ADqEz/OAGbTyJJIY4+tT4/khwPvh857A7DDOHuHznomMLWw3gmAlXkvkZ1fjvQZR/yS737D+JLsPE/HZpcoj/HttS5Ps9iVYHjs/6lhijDem37FkfIS/U48Af486lhb+OXajGZPs2k0P3ltQIjds9wTBL2UFMNbXH+ZNRGw3AZ2B/4a9lh/c/UBgE+A+M6simFNxvbt/1VqxNSG+M83sQILPZznBrHrcfbmZXUWwBgHAle7e0ISzeMQGwd/lYx7+toTi/tkBmNmjBL+geWY2H7gcyAzjv5egquP+BJM3S4A/hvvi+tlJZO4k6Cy8Fv4uv+/up0QbUv3q+x2LOKxY7QgcD0w3s8/CbRd7UE015amSnYiISApqT7PoRURE2g0leBERkRSkBC8iIpKClOBFRERSkBK8iIhIClKCFxGJAzPLrbFS4SIzWxA+X2lmrX47ZiOxHGxmI2u8vjIsnNXU8wyqb1XERDCzi2u9fi/8GWlcyUoJXkQkDtx9mbuPcvdRwL3AbeHzUTSztnhDwuqb9TmYYFXM6tguc/fXWzuGBFgvwbv7DlEF0hYowYuIJF66mf0zXKP8VTPrBGBmQ8zsFTP72MzeNrMR4fZBZva/cDGnN8xsQLj9ITO718w+AG6s63gz2wE4ELgpHEEYEh53eHiObczsPTObZmYfmlmX8P3eNrNPwkeDidQCd1qwZvzrZjahxvnnmFle+LwgLAmNmW1rZlMsWOv8PTMbHm4/wcyeDv8cM8zsxnD79UCn8M8wPtxWVEcs6WZ2k5l9FH5eJ4fb+5jZ5PD4L8xs5xb+HSa9dlPJTkQkiQwFjnb3P4VVMg8D/g2MA05x9xlmNhq4m2DRpn8AD7v7w2b2f8AdBL1yCGrD7+DulWb2Ru3j3X0PM3ueoNTpkwBhBT3MLAt4HDjS3T8ys67AWmAJsLe7rzOzoQRLLBc08Oc5BBhOMEqwAUHVzwcb+Qy+AXYOK+XtBVwbfg4QjHJsCZQC35rZP9z9QjM7PRwFaciJwCp338bMOgDvmtmrwKHARHe/xszSgexGztPmKcGLiCTebHf/LHz+MTDIghXPduDnctQQlLQF2J4gQQH8i58XcwL4b5jcGzq+PsOBH939IwAPV1ozsxzgTjMbBVQCwxo5zy4Eq0lWAgvN7H+NtIdgMa+Hwy8QTli+OfSGu68KY/kKGMj6S9Y2ZB9g8+oRhPB9hhKUfH7QgsVnnq3x+acsJXgRkcQrrfG8EuhEcMl0ZQw91NqKw5/NPb4uZwOLgS3C865rwbkq+PlycMca268C3nT3QyxYq31SjX21P5+m5CoDznD3ib/YYbYLweJOD5nZre7+SBPO2+boGryISBIIe8+zzewI+Om69hbh7vcIF78CjgXebuLxa4Audbztt0AfM9smPKaL/bxU9o/uXkWwWEt6I+FPBo4Mr3/3AXavsW8OsHX4/LAa27vx87KzJzRy/mrlYQ+8IROBU6vbmdkwM8sxs4HAYnf/J8GS1lvF+J5tlhK8iEjyOBY40cymAV8CB4XbzwD+aGafEyTcs5p4/GPA+eGEtiHVjd29DDgS+Ed4zGsEvey7gT+E20bw8yhBfZ4hWG75K4LlWafU2Pc34HYzm0rQG692I3CdmX1K7D30ccDn1ZPs6nF/GMcnFtw6d194/t2AaeH7HQncHuN7tllaTU5ERFqVmT1EjUl9Eg314EVERFKQevAiIiIpSD14ERGRFKQELyIikoKU4EVERFKQEryIiEgKUoIXERFJQUrwIiIiKej/Az7+gCf5PRgqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "user_repeat_buy = [rate for rate in train_data.groupby(['user_id'])['label'].mean() if rate <= 1 and rate > 0] \n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "\n",
    "ax=plt.subplot(1,2,1)\n",
    "sns.distplot(user_repeat_buy, fit=stats.norm)\n",
    "ax=plt.subplot(1,2,2)\n",
    "res = stats.probplot(user_repeat_buy, plot=plt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出近6个月，用户复购率很小，基本买一次为主"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_user_info = train_data.merge(user_info,on=['user_id'],how='left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 查看用户性别与复购的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "plt.title('Gender VS Label')\n",
    "ax = sns.countplot('gender',hue='label',data=train_data_user_info)\n",
    "for p in ax.patches:\n",
    "    height = p.get_height()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 查看用户性别复购的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EeCWvA6jqOFXtoao96gbJtIrGhxYvhmuuccJ67lwnvCs4C25jgl8a0NTjdRN3WZ7buOe1o4CDOK3zBaqarqqngGlAN3fdKWCyu/+X7nJjys6SJU5Lu2FDZ/IQC23AgtuYimA50FpEYkUkArgdmJprm6nAGPf5zcAcVVVgBtBRRKq4gd4f2Oiu+xYY4O4zGNiIMWVlyRKnpd2ggbW0c7Gryo0JcqqaKSIP4YRwKPC+qm4Qkb8CK1R1KvAe8LGIJAKHcMIdVT0sIq/hhL8C01T1e/fQv3f3eQM4ANzjy89lgtjSpU5o16/vhHbj3JdkVGwW3MZUAKo6Daeb23PZ0x7PTwO35LPvJzi3hOVevhO4omwrNRXe0qVw9dUW2gWwrnJjjDHlQ3y809KuV88J7SZN/F1RuWTBbYwxxv/i452Wdt26zoVoFtr5suA2xhjjX56hbS3tQtk5blNiWZmZbFqxkF2JGwkNDSW2fTdaduyBM+CWMcYUwbJlTmjXqeOEdtOmhe9TwVlwmxLZu3M7X/37eQ7tSyOiUmWys7JYOmMyTVu3Z+SDT1GjVh1/l2iMKe+WL78Q2vPmWWgXkQW3KbY1a9bw4Qu/o1KVatz2/56ldadeZGVlsm7xHH787B0++NtvGPPUa9Ssk+cImMYY44T2VVdB7drW0i4mO8dtimX37t088MADVIuqxX1P/5O4Ln2QkBDCwiPo2n8IY/7wKmcyMvjstT9x9sxpf5cb1LKzszl27Ji/yzCm+DxDe948aNbM3xUFFK8Gt4jUFJFJ7ly9m0TkUhGpLSIzRWSb+7WWN2swZSc7O5snnniCzMxMRv3uearXiv7ZNg2at+Tmh/5E+p5Ufvz0HT9UGdzuuOMOjh07xsmTJ+nQoQPt2rXj5Zdf9ndZxhTdihUW2qXk7Rb3m8B0VW0DdAY2AU8Cs1W1NTDbfW0CwLfffsvKlSv5wx/+QHSD/AdFaNG+G5cOuZnV839g5+YEH1YY/DZu3EiNGjX4+uuvufbaa0lKSuLjjz/2d1kmQE2YADExEBLifJ0wwctvuHKlE9q1ajnd4xbaJeK14BaRKJxRld4DUNWzqnqEi+f9HQ+M8FYNpuycPHmSV155hU6dOnHjjTcWun3/EXdSs059pk/4N5qd7YMKK4Zz585x7tw5vv76a2644QbCw8PtKn5TIhMmwNixsHMnqDpfx471YnivXAlXXgk1azot7ebNvfRGwc+bLe5YnPGLPxCR1SLyPxGpCtRX1T3uNnsBm8M3ALz77rukp6fz1FNPERJS+I9NeGQl+o8cw/7UJDatXOSDCiuGX/3qV8TExHDy5EmuuOIKdu7cSY0aNfxdlglAf/wjnDp18bJTp5zlZc4ztOfOtdAuJW8GdxjONH//UdWuwElydYu7MwxpXjvbHL7lx9GjR5kwYQJDhw6lc+fORd6vQ58BRDdowoJvPrFWdxl55JFHSEtLY9q0aYgIzZs3Z+7cuf4uywSglJTiLS+xVauc7vGoKCe0Y2LK+A0qHm8G9y5gl6rGu68n4QT5PhFpCOB+3e/FGkwZmDhxIqdOneL+++8v1n4hIaFcPnw0B3Yls3HFT16qrmLZt28f9913H9deey3gnPMeP358IXsZ83P5nV4u09POq1Y5Le0aNZzucQvtMuG14FbVvUCqiFziLsqZr9dz3t8xwDfeqsGU3tmzZ/n444+57LLLaNOmTbH3b9+7P7UbNGHp9K+8UF3Fc/fdd3PNNdewe/duAOLi4njjjTf8W5QJSM8/D1WqXLysShVneZlYvdpC20u8fVX5w8AEEUkAugAvAP8ArhKRbcCV7mtTTk2dOpX09HTuvffeEu0fEhJKj0HXs3vHFvYkbyvj6iqe9PR0br311vPXGYSFhREaGurnqkwgGj0axo1zTjeLOF/HjXOWl9rq1TB4MFSvbqHtBV4NblVd456n7qSqI1T1sKoeVNXBqtpaVa9U1UPerMGUzhdffEHr1q257LLLSnyMzn2vIiwikpVzvivDyiqmqlWrcvDgwfNXki9dupSoqKgi7SsiQ0Rki4gkisjPbsMUkUgRmeiujxeRGI91nURkiYhsEJF1IlIp175TRWR9qT6c8bnRoyE5GbKzna9lEtpr1jgtbQttr7GR00y+tm7dyrp167jppptKdctRparV6NB7AOuXzuX0qZNlWGHF89prr3HDDTewfft2+vbtyy9+8Qv+9a9/FbqfiIQCbwPXAu2AUSLSLtdm9wGHVbUV8DrwortvGPAJ8ICqtgcGAOc8jj0SOFHqD2cC35o1Tku7alXnQrTYWH9XFJQsuE2+Jk+eTHh4OMOGDSv1sboPup5zZ8+wbvHsMqis4urWrRvz589n8eLFvPvuu2zYsIFOnToVZddeQKKq7lDVs8DnOGMqePIcY2ESMFicv9iuBhJUdS2A22uWBSAi1YDfAc+V+sOZwOYZ2vPmQYsW/q4oaNkkIyZPZ8+eZerUqQwcOJDatWuX+niNYuOo1zSW9Uvn0PPKG8qgworpo48+uuj1qlWrAPjFL35R2K6NgVSP17uA3vlto6qZInIUiAbiABWRGUBd4HNVfcnd52/Aq0CuO4IvEJGxwFiAZjZSVnBau9ZC24csuE2e5s+fz+HDhxk5cmSZHbNDn4HM+fJ9Du/fQ616DcvsuBXJ8uXLzz8/ffo0s2fPplu3bkUJ7tIIA/oBPXECeraIrAQOAi1V9bee58NzU9VxwDiAHj165DlugwlgOaFdpYrTPW6h7XUW3CZP06ZNo3bt2vTt27fMjtm+9wDmfPk+G+Ln0W/YqDI7bkWS+3z2kSNHuP3224uyaxrgOW9iE3dZXtvscs9rR+GE8y5ggaqmA4jINJwxGU4APUQkGef/knoiMk9VBxTvU5mAlZDghHblyk5Lu2VLf1dUIdg5bvMzGRkZzJ8/n6uuuoqwsLL7265mnfo0jWvPuiVzcAbNM6VVtWpVkpKSirLpcqC1iMSKSARwO86YCp48x1i4GZjjjm44A+goIlXcQO8PbFTV/6hqI1WNwWmRb7XQrkASEmDQICe058610PYha3Gbn1mwYAEZGRnnR+cqSx36DOSHj95iX+oOGjSzX/TiGjZs2Pkr/LOzs9m4cSO33nprofu556wfwgnhUOB9Vd0gIn8FVqjqVJwJgT4WkUTgEE64o6qHReQ1nPBXYJqqfu+Fj2cCxbp1Tku7UiUntFu18ndFFYoFt/mZ6dOnEx0dTY8ePcr82G17XM70j//N5pWLLLhL4LHHHjv/PCwsjObNm9OkSZMi7auq04BpuZY97fH8NHBLPvt+gnNLWH7HTgY6FKkQE9jWrXNa2pGRTve4hbbPWXCbi+R0kw8fPtwrI3JVrVGTpnHt2bJyMQNu9OoFVUGpf//+/i7BVGTr1zuhHRFhLW0/snPc5iI//fQTGRkZXHPNNV57j0u6Xcb+XUkc2r/ba+8RbKpXr06NGjV+9shZbozXrV8PAwc6oT1vHrRu7e+KKiwLbnORuXPnEhUV5ZVu8hyXdHOGT926aonX3iPYHD9+nGPHjv3skbPcGK/ybGlbaPtdkYJbRCaLyFARsaAPYllZWcyfP5/LL7+8TK8mz61W3QbUb9qCzasWee09gt3+/ftJSUk5/zDGazZscEI7LMzpHrfQ9ruiBvG/gTuAbSLyD4+pOk0QSUhI4PDhwwwYMMDr73VJt8tI3baRk8eOeP29gsnUqVNp3bo1sbGx9O/fn5iYGK9c/W8M4IT2wIFOaM+bB3Fx/q7IUMTgVtVZqjoaZ9CFZGCWiCwWkXtEJNybBRrfmTt3LmFhYVx++eVef6+4rn1Ale3rVnj9vYLJn//8Z5YuXUpcXBxJSUnMnj2bPn36+LssE4w2brzQ0rbQLleK3PUtItHA3cD9wGrgTZwgn+mVyozPzZ07l+7du/vkYqcGzVpStUZNEtctL3xjc154eDjR0dFkZ2eTnZ3NwIEDWbHC/vgxZWzjRqelHRrqdI9baJcrRTqRKSJTgEuAj4FhqrrHXTVRROx/jSCQlpZGYmIiN910k0/eT0JCaNGhO4kJy8nOziIkpOxvPQtGNWvW5MSJE1xxxRWMHj2aevXqUbVqVX+XZYJJTks7JMQJ7UvszGh5U9QW939VtZ2q/j0ntEUkEkBVvXf5sfGZRYucC8WuuOIKn71nq049yThxjD3J23z2noHqyy+/5PTp03zzzTdUqVKF119/nSFDhtCyZUu+/fZbf5dngsWmTU5oi1hol2NFDe685tq1e3mCyMKFC2nYsCGxPpz4vkX7biBi57mL4NNPP6VZs2Y88MADTJ8+HRFhzJgxPPLII0RHR/u7PBMMNm1yusdzQrtNG39XZPJRYHCLSAMR6Q5UFpGuItLNfQwAqviiQON9mZmZLF26lL59+54fB9sXqlSPolFsHIkJFtyFmTJlComJiVx55ZX861//okmTJjzwwAPMnz/f36WZYLB5sxPaYKEdAAo7x30NzgVpTYDXPJYfB57yUk3Gx9atW8fx48fLdArPomrZsQcLp37GqRPHqFLNRgArSI0aNRgzZgxjxozh4MGDTJo0iUceeYRDhw6Rmprq7/JMoLLQDjgFtrhVdbyqDgTuVtWBHo8bVHWyj2o0XrZo0SJCQkL8cltRq449Uc0macMqn793oDp8+DCTJ09m4sSJHDp0iJtvvtnfJZlAtWWLE9qqMGcOtG3r74pMERTY4haRO91ZgWJE5He516vqa3nsZgLMokWL6NChAzVr1vT5ezdqEUelqtVITFhB+94DfP7+geLEiRNMmTKFzz77jNWrV3PDDTfw5z//mQEDBvj09IYJIlu2wIABkJ3ttLTbtfN3RaaICusqz7nPpJq3CzH+cezYMRISEvjVr37ll/cPCQmlRfvu7Fi/ElW1EMpHTEwMQ4YM4cEHH+Saa64hPNzGPTKlkNPSttAOSAUGt6q+63591jflGF9bunQp2dnZfjm/naNF+25sXDafA7t3Uq9xjN/qKM9SU1OpXLlyifcXkSE4gyaFAv9T1X/kWh8JfAR0Bw4Ct7lzbCMinYB3gRpANtAT5zTbl0BLIAv4VlWfLHGBxne2bnVCOyvL6R630A44RZ1k5CURqSEi4SIyW0QOiMid3i7OeN+iRYuoVq0anTp18lsNse26AJC8ca3faijvShnaocDbwLVAO2CUiOT+3/o+4LCqtgJeB1509w0DPgEeUNX2wADgnLvPK6raBugK9BURGzS9vNu61ekez8x0Qrt9e39XZEqgqPdxX62qx4DrccYqbwU87q2ijO8sWrSI3r17+7XrtWbdBtSs24DkTWv8VkOQ6wUkquoOVT0LfA4Mz7XNcGC8+3wSMFic8xZXAwmquhZAVQ+qapaqnlLVue6ys8AqnLtPTHm1bZvT0s7MdLrHLbQDVlGDO6dLfSjwpaoe9VI9xod27dpFWloal156qb9LIaZtF3ZuTiA7O8vfpQSjxoDn/WK73GV5bqOqmcBRIBqIA1REZojIKhF5IvfBRaQmMAyYndebi8hYEVkhIisOHDhQ2s9iSmLbNqelfe6ctbSDQFEnXf5ORDYDGcCvRaQucNp7ZRlfiI+PB6BXr15+rgRi2nZmzYLp7N25nUaxNqFBbsOGDSvwwr2pU6d6663DgH4457VPAbNFZKWqzobzXemfAf9U1R15HUBVxwHjAHr06KHeKtTkIye0z551WtodOvi7IlNKRQpuVX1SRF4Cjqpqloic5OddbSbALFu2jOjoaFq1auXvUoht2wWA5E1rLLjz8NhjjwEwefJk9u7dy513OpeYfPbZZ9SvX7+w3dOAph6vm7jL8tpmlxvGUTgXqe0CFqhqOoCITMOZFTCndT0O2Kaqb5TogxnvSkx0uscttINKUVvcAG1w7uf23OejMq7H+IiqsmzZMnr27FkubsGqVrM2dRo1I3njGi677lZ/l1Pu9O/fH4BHH330omk8hw0bRo8ehc7zsxxoLSKxOAF9O3BHrm2mAmNw5iC4GZijqioiM4AnRKQKcBboj3PxGiLyHE7A31+qD2e8IzHRaWmfOeN0j1toB42iXlX+MfAKF7rMegI2K1gAS0lJYe/eveWimzxHbNsupGxdT1bmucI3rqBOnjzJjh0XeqSTkpI4efJkgfu456wfAmYAm4AvVHWDiPxVRG5wN3sPiBaRROB3wJPuvodxhjteDqwBVqnq9yLSBPgjzlXqq0RkjYhYgJcXOS3t06dh9mzo2NHfFZkyVNQWdw+gnara+akgkXN+u3fv3n6u5IKYdl1YPnsqaTu20CzOWgd5ef311xkwYAAtWrRAVdm5cyfvvvtuofup6jRgWq5lT3s8Pw3cks++n+DcEua5bBfg/64a83PbtzuhnZHhtLT9eKun8Y6iBvd6oAGwx4u1mDLwaXxKkbab/MNcqkXVZsn+UJYeKNo+3ta8TScQIWnjGgvufAwZMoRt27axefNmANq0aUNkZKSfqzLlxvbtTve4hXZQK+rtYHWAje4tIVNzHt4szHiPqrJzcwLN23by6fntwjpsKletTsPmrex+7gKcOnWKl19+mbfeeovOnTuTkpLCd9995++yTHmwY8eFlvbs2RbaQayoLe5nvFmE8a2De3dx4sghYtp09vp77T16mlUph9l58CR7jp4mM1sJCxEaRlUitk41esXWpnbViPPbx7TtQvyPUzh35jThkZW8Xl+gueeee+jevTtLliwBoHHjxtxyyy1cf/31fq7M+NWOHU5L++RJp6Xd2fu/28Z/ino72HwRaQ60VtVZ7hWmod4tzXjLzk3O0KLNvRjcqYdOMX3DXpLSTxIqQrPoKvSKrU1kWChnM7PYdTiDhYkHWJh4gC5Na3Jdh4ZUiQwjtl0XlvzwJanbNtCiQ3ev1Reotm/fzsSJE/nss88AqFKlSqE9GSbIeYb27NkW2hVAkYJbRH4JjAVq40wq0Bh4BxjsvdKMtyRvXkv1WnWoXb9RmR/79LksvkvYw6qUw1SNDOO6Dg3o2qwWVSN//qN2NOMcixLTWbL9IIn7T3Brj6Y0jetASGgoSRvXWHDnISIigoyMjPOnOLZv327nuCuypCSnezwntLt08XdFxgeK2lX+fzjjHccDqOo2EanntaqM1+Sc327RvnuZn9/eefAkE5encjTjHP3j6jIgri6R4fl3zERVDue6jg3p3LQmE5en8v6iJG7r2YzGLdqQvNkmHMnLs88+y5AhQ0hNTWX06NEsWrSIDz/80N9lGX9ISnJa2sePW2hXMEUN7jOqejbnP3p3EJYi9c+5MxOtANJU9Xp3EIjPccZBXgnc5U5SYHwgfXcKJ48dIaZt2XanLU86xNS1u4mqEs6v+rekWe0qRd63cc3KPDigJeMXJ/P5shQ6NrqExJ++5kzGSSIrVy38ABVEdnY2hw8fZvLkySxduhRV5c0336ROnTr+Ls34WnKy09LOCe2uXf1dkfGhol5VPl9EngIqi8hVOPPwflvEfX+DM+hDjheB193pAw/jTCdofCQ55/x2GQW3qjJ9/R6mrEmjRd2q/N+AVsUK7RyVwkO5p28sMXWqsjGzHpqdTcqW9WVSY7AICQnhpZdeIjo6mqFDh3L99ddbaFdEyclOS/vYMQvtCqqowf0kcABYB/wKZyCHPxW2kzu60lDgf+5rAQbhTBsIzjSCI4pVsSmV5M1riYquR806hY5vXaisbGXK6jQWbEunV2xtxlwWQ+WIkl+zGBEWwp29mxPVpDWEhLJ53apS1xhsrrzySl555RVSU1M5dOjQ+YepIDxDe9YsC+0KqqhXlWeLyNfA16panHn53gCeAKq7r6OBI+4QjJD39IKAMxUgzgVxNGvWrBhvafKj2dns3JxA6869S31+OzM7m8+XpbJxzzEGtanH4Db1yuSceeWIUMZc3pr/zI5h3ZpVXDfarpj2NHHiRADefvvt88tE5KJhUE2QyukeP3rUaWl36+bvioyfFNjiFsczIpIObAG2iMgBEXm6oP3cfa8H9qvqypIUpqrjVLWHqvaoW7duSQ5hctmflkzGiWOlPr+dla3nQ/v6Tg25sm39Mr3QrU61SNp06krmwVR+XGOB5CkpKelnDwvtCmDnTie0jxxxWtoW2hVaYV3lvwX6Aj1Vtbaq1gZ6A31F5LeF7NsXuEFEknEuRhsEvAnU9JhhLK/pBY2X7NycAFCq4M7KViaucEJ7WKeGXNbSO+dYe/XqjaAsXrqclTsPe+U9AtGpU6d47rnnGDt2LADbtm2zkdOC3c6dTvd4Tmh3t9skK7rCgvsuYJSqJuUsUNUdwJ3ALwraUVX/oKpNVDUGZxrBOao6GpiLM20gONMIflPC2k0xJW9aS626DYmKLtmdfNmqTFqZyvq0o1zXoQGXeim0ARq3bENYeASVjuzgj1PWkZmV7bX3CiT33HMPERERLF68GHBGTvvTnwq93MQEqpSUCy3tmTMttA1QeHCHq2p67oXuee7wEr7n74HfudMHRuNMJ2i8TLOz2bklwZnIowSyVZm8Ko21u45yTbv69Gvt3dMXYeERNGnVjurHktm89zjjl+z06vsFiu3bt/PEE08QHu78+tnIaUEsJcVpaR865IR24fOumwqisOAu6P7qIt97rarzVPV69/kOVe2lqq1U9RZVPVPU45iS25u6g9MnT5ToNrBsVb5Zk8aqlMMMblOP/pf4ZuydmLadObovhb5NK/P6zK3sP3baJ+9bntnIaRWEhbYpQGHB3VlEjuXxOA7YzOwBJGd88phitrhVle8S9rA8+TAD4uoyqI3vBsxr3qYTqHJdw1Ocyczi9VnbfPbe5VXukdMGDx7MSy+95O+yTFlKTXW6x3NCu2dPf1dkypkCg1tVQ1W1Rh6P6qpa0q5y4wfJmxOoXb8xNWoXvYtbVflh/V6W7jhIv1Z1uKpd2V49XpjGLS4hPCKS5E1rGd27OV+sSCVx/wmfvX95dNVVVzF58mQ+/PBDRo0axYoVKxgwYECR9hWRISKyRUQSReTJPNZHishEd328iMR4rOskIktEZIOIrBORSu7y7u7rRBH5p/jyByQYpaY6Le30dPjxRwttk6eiDsBiAlh2dhYpW9YV+2rymZv2sTAxnT4torm2QwOfhjZAaFg4TVu3Jz4+nocHtaJyeCgvz9js0xrKi1WrVp1/7Ny5k4YNG9KoUSNSUlJYtarwgWrcoYffBq4F2gGjRKRdrs3uAw67oxq+jjPKYc4Qx58AD6hqe2AAcM7d5z/AL4HW7mNIKT9qxZXT0k5Pd1ravXr5uyJTThV1rHITwPbu3M6ZjJPFujBtzub9zNtygJ4xtbi+U0Ofh3aO5m07M3fSB8jZk/zqiha8OnMra1OP0LlpTb/U4y+PPvooAKdPn2bFihV07twZVSUhIYEePXqcn5+7AL2ARPeuEETkc2A4sNFjm+HAM+7zScBbbgv6aiBBVdcCqOpB9xgNgRqqutR9/RHOSIg/lPLjVjy7djmhfeCA09K20DYFsBZ3BXB+fPIiBveCrQeYtWkfXZvWZHiXxoT4sfczxp0zfPny5dzTL5aoyuG8NTfRb/X4y9y5c5k7dy4NGzZk1apVrFixgpUrV7J69WoaN85z8MHcGgOpHq/zGrXw/Dbu6IZHce78iANURGaIyCoRecJj+12FHBMRGSsiK0RkxYEDxRl4sYLYtcvpHs8J7d69/V2RKecsuCuAnZsTiG7YlOo1owvddvH2dKZv2EvHxlGM7NbEr6EN0DCmNVWqVCE+Pp5qkWHc0zeGmRv3sWnPMb/W5S9btmyhY8cL14V26NCBTZs2FbBHmQgD+gGj3a83isjgou5soyAWIKelvX8/zJhhoW2KxII7yGVnZZGydX2Rzm8vSzrEdwl7aNewBrf2aEpoiP+vMwoNC6N79+4sW7YMgLsvi6FaZBhvV8BWN0DHjh25//77mTdvHvPmzeOXv/wlnToVqSclDWjq8TqvUQvPb+Oe144CDuK0pBeoarqqnsKZZKibu32TQo5p8pOW5oT2vn1OS7tPH39XZAKEBXeQ25O8jbOnTxXaTb5q52G+XpPGJfWrc3uv8hHaOXr16sX27ds5cOAANatEcNelzfl+3R62H6h4V5h/+OGHtG/fnjfffJM333yTdu3a8cEHHxRl1+VAaxGJFZEInNEMp+baZirOaIbgjG44R53RXWYAHUWkihvo/YGNqroHOCYifdxz4b/ARkIsmrQ0p3vcQtuUgF2cFuSSz9+/nX+Le23qEb5atYtWdatxR+9mhIWUr7/nervdh8uWLWPo0KHc1y+WDxYl8e+523n11rKZVzwQZGVlce211zJ37lx++9vCpgq4mKpmishDOCEcCryvqhtE5K/AClWdijOK4cfuqIaHcMIdVT0sIq/hhL8C01T1e/fQDwIfApVxLkqzC9MK49nSnjHDQtsUmwV3kNu5eS11Gzenao2aea5fn3aUL1em0jy6Knf2aU54aPkKbYC2bdtSrVo1li9fztChQ6lTLZJRvZrx8ZKdPHZNHA2jKvu7RJ8IDQ0lJCSEo0ePEhUVVez9VXUaTje357KnPZ6fBm7JZ99PcG4Jy718BdCh2MVUVLt3O6G9Z48T2pde6u+KTACy4A5iWZmZpGzbQJd+V+e5fvOeY0xcnkqTWlUYc2lzIsLKX2gDhLnnuePj488vu7dvLOMXJ/PRkp38fkgbP1bnW9WqVaNjx45cddVVVK1a9fzyf/7zn36syhTJ7t1O93hOaF92mb8rMgHKgjuI7U7awrkzp/Mcn3zbvuNMWJZCg6hK3H1ZDJHhoX6osOh69+7N/Pnz2b9/P/Xq1aNp7Spc3a4Bn8an8Mig1lSOKN/1l5WRI0cycuRIf5dhiit3S9tC25SCBXcQO3//9iUXDyu/48AJPl66k3rVI7mnbwyVynlog3OBGkB8fDzDhg0D4L7LY5m+YS9frdrFnX2a+7M8n7nttttITHSuqG/VqhWVKlXyc0WmUHv2OKG9ezdMn26hbUqtfPaNmjKxc/Na6jWNpUr1C+dDdx48yUdLdlK7agT39I2lSkRg/O3Wpk0batSocf62MIAezWvRqUkU7y9KIjs7uKe2zMzM5IknnqBJkyaMGTOGX/ziFzRt2pQnnniCc+fOFX4A4x85oZ2WBj/8AH37+rsiEwQsuINU5rmzpCZuuuhq8l2HT/Hh4mRqVA7jvn6xVIsMjNAG58Ksnj17XhTcIsK9fWPZceAk87cF94hcjz/+OIcOHSIpKYmVK1eyatUqtm/fzpEjR3jsscf8XZ7JS05o79rltLT79fN3RSZIWHAHqd07tpB59sz5gVf2Hj3NB4uSqRIRyn39WlC9UuBN7tazZ09SUlLYs2fP+WXXdWxI/RqRvL8wyY+Ved93333Hf//7X6pXr35+WY0aNfjPf/7DtGnTCtjT+MXevTBokIW28QoL7iCVvHktiNDsko6knzjD+4uSCA8V7uvXgqjKgRfacOF+bs+ryyPCQhjduzk/bUsnOf2kv0rzOhHJc6KX0NBQv00AY/Kxd6/T0k5NdbrHLbRNGbPgDlLJm9bSoFlLzkgk7y1MIluVe/vFUrtqhL9LK7G4uDhq1qx5UXc5wO09mxIWIkyI3+mnyryvXbt2fPTRRz9b/sknn9CmTcW5Ha7cy2lpp6bCtGlw+eX+rsgEocA5yWmKLPPsWXYlbqLzgKG8tzCJM5lZ3N+vBfWqB/YVyCEhIT87zw1Qr0YlrmnfgC9X7uLRqy8JiKvki+vtt99m5MiRvP/++3Tv3h2AFStWkJGRwZQpU/xcnQGckdAGDYKUFCe0r7jC3xWZIGUt7iCUmriBrMxzbM5qwPHTmdx9aQyNagbH6GK9e/cmLS2NtLSL57IY3acZR06d47uEPfnsGdgaN25MfHw8Tz/9NDExMcTExPD000+zbNmyok7rabxp3z6ne3znTgtt43XW4g5C29evAgnhSLVmjOnTnGbRVQvfKUD07NkTcM5zew5EcmmLaFrWrconS3dyc/cm+e0e8AYNGsSgQYP8XYbxlNPSttA2PmIt7iCTrcqq5fFk1W7OTb1a0qpeNX+XVKZat25N7dq1L7pADZyLt+7s05w1qUdYn3bUT9WZCicntJOT4fvvoX9/f1dkKgAL7iAzbeV2Th9IoUW7bnRpWtPf5ZQ5EaFXr14sW7YMZ8bJC0Z2a0Ll8FA+WRq8F6mZcmT//otDe8AAf1dkKggL7iDyaXwK8UuXICgD+wfvLSh9+vRh7969JCcnX7Q8qnI4w7s04ps1uzmaYaOJGS/KCe2kJAtt43MW3EFi6Y6D/Pmb9dQ6kUxk5ao0jr3E3yV5TV932MhFixb9bN2dfZqTcS6Lyat2+bosU1Hs3w+DB8OOHRbaxi8suIPA7iMZ/N+EVTSrXZmwA9uIaduZkNDguyUqR5MmTWjWrBkLFy782boOjaPo3LQmE+JTftaVXtGJyBAR2SIiiSLyZB7rI0Vkors+XkRi3OUxIpIhImvcxzse+4wSkXUikiAi00Wkjg8/ku8dOOCE9vbt8N13zpXkxviYBXeAO30uiwc+WcmZzGyeHdSAowf30aJ9N3+X5XX9+vVj2bJlnD179mfrRvduRuL+EyxLOuSHysonEQkF3gauBdoBo0SkXa7N7gMOq2or4HXgRY9121W1i/t4wD1mGPAmMFBVOwEJwENe/ij+c+CA0z2eE9p2db/xEwvuAKaq/Onr9STsOsqrt3YmbcsaAGIrQHD37duXjIwMVq9e/bN1wzo1okalMCbEp/ihsnKrF5CoqjtU9SzwOTA81zbDgfHu80nAYCl4PFVxH1Xd7WoAu8u27HIip6WdmAjffmuhbfzKgjuATYhPYdLKXTwyqBXXtG/AkiVLiIquT+36jfxdmtf17t2bsLCwPLvLK0eEclP3Jvywfg/pJ874obpyqTGQ6vF6l7ssz21UNRM4CkS762JFZLWIzBeRy91tzgG/BtbhBHY74L3cbywiY0VkhYisOHAgAGdxS093QnvbNqelPXiwvysyFZwFd4DavPcYf/1uI/3j6vL/rowjMzOTpUuXEtu+a4WYdKJq1ap07do1zwvUwOkuP5elfLnCLlIrA3uAZqraFfgd8KmI1BCRcJzg7go0wukq/0PunVV1nKr2UNUedevW9WXdpZee7rSut21zWtoW2qYcsOAOQBlns3j409XUqBTOq7d2JiREWL16NcePH6dVp57+Ls9n+vbty6ZNm0hPT//Zulb1qtM7tjafLttJdrZdpAakAU09Xjdxl+W5jXv+Ogo4qKpnVPUggKquBLYDcUAXd9l2da4E/AK4zIufwbc8W9rffgtXXunviowBLLgD0nPfb2Tb/hO8dmtn6lSLBGDBggWEh4fTon1XP1fnO/3c6RKXLFmS5/rRfZqTeiiDnxJ/HuwV0HKgtYjEikgEcDswNdc2U4Ex7vObgTmqqiJS1724DRFpAbQGduAEfTsRyWlGXwVs8vLn8I30dCeot26FqVMttE25YsEdYKav38uE+BTGXtGCK+IudDsuWLCA7t27E1k5eMYlL0zbtm2pVatWnue5AYa0b0B01Qgm2EhqOeesHwJm4ITrF6q6QUT+KiI3uJu9B0SLSCJOl3jOLWNXAAkisgbnorUHVPWQqu4GngUWiEgCTgv8BV99Jq85eNAJ6i1b4Jtv4Kqr/F2RMRexSUYCyJ6jGfz+qwQ6No7isasvDLCyZ88etm7dyhNPPOHH6nwvJCSEvn37snDhQrKysgjNde96RFgIt/ZsyrgFO9hzNIOGUcExQ1pJqeo0YFquZU97PD8N3JLHfl8BX+VzzHeAd/JaF5AOHnS6xzdvdlraV1/t74qM+RlrcQcIVeX3X63jbGY2/xzVlYiwC/90P/30EwBXVMBZiQYMGMChQ4dISEjIc/2ons3IVuXzZal5rjfmvJyWtoW2KecsuAPEFytSWbD1AE9e24bYOhd3h8+fP5/GjRvTokULP1XnP/369SM0NJT58+fnub5ZdBWuaF2Xz5enkJmV7ePqTMA4dMjpEt+0yeket9A25ZgFdwBIO5LB377bRJ8WtbmrT/OL1p09e5YlS5ZwxRVXVIjbwHKLioqie/fuzJ07N99t7uzTnH3HzjB7834fVmYCxqFDTkt740b4+mu45hp/V2RMgSy4yzlV5cmvEshW5eWbnVu/PC1fvpyMjIwK2U2eY8CAAWzdupW0tNx3NzkGXlKXhlGVbCQ183O5Q3vIEH9XZEyhvBbcItJUROaKyEYR2SAiv3GX1xaRmSKyzf1ay1s1BIPPlqXy07Z0/nBdW5rWrvKz9QsWLCAyMpLevXv7obryYYA7O9O8efPyXB8WGsLtPZuxYOsBUg6e8l1hpnzL6R7fsMFC2wQUb7a4M4FHVbUd0Af4P3dSgyeB2araGpjNhVtOTC57jmbwwrRNXNYymtG9mv1svaoye/ZsevfuTeXKFfeK6djYWGJiYvINboDbejYlNESYsMxuDTPA4cNOaK9fb6FtAo7XgltV96jqKvf5cZx7Rxtz8UQG44ER3qoh0P3lmw1kZmfzj5GdftZFDrBp0ybS0tK42i6kYeDAgcTHx3Py5Mk81zeIqsSVbevx5YpdnMnM8nF1plw5fNjpHl+/HqZMgWuv9XdFxhSLT85xu/P6dgXigfqqusddtReo74saAs2MDXv5ceM+/t+VcTSL/nkXOcCsWbMICQlhoM0JzIABAzh37ly+Y5eDc5HaoZNnmb5+rw8rM+WKZ0t7yhS47jp/V2RMsXk9uEWkGs7gDf9PVY95rnPHN85zIOmAn1GoFI6fPsdfvtlAmwbVua9fbL7bzZw5kx49elC7dm0fVlc+de3alaioKGbPnp3vNn1b1qF5dBW7SK2iOnLEuc1r3TqYPNlC2wQsrwa3O3vQV8AEVZ3sLt4nIg3d9Q2BPO/RCegZhUrp1R+3su/4af4+siPhoXn/EyUlJZGYmMhVNhwjAOHh4QwaNIg5c+Zw9uzZPLcJCRHu6NWMZUmH2LbvuI8rNH515IjT0k5IgK++gqFD/V2RMSXmzavKBWfs402q+prHKs+JDMYA33irhkC0JvUI45ckc1ef5nRtlv8F97NmzQJgsE0zeN6QIUM4ceJEgd3lN3dvQkRoiLW6K5Kc0F671gnt66/3d0XGlIo3W9x9gbuAQSKyxn1cB/wDuEpEtgFXuq8NkJmVzR8mr6Ne9Ugev+aSAredOXMmnTp1omHDhj6qrvzr06cPUVFRTJ8+Pd9toqtFMrRTQyat3MWx0+d8WJ3xi5zu8bVrne5xC20TBLx5VflCVRVV7aSqXdzHNFU9qKqDVbW1ql6pqoe8VUOgeX9REpv2HOPZG9pTvVJ4vtvt2bOHdevWcaVNNXiRiIgIBg8eXGB3OcC9fWM5cSaTL5bb+OVB7cgRZxS0NWuspW2Cio2cVk6kHjrF6zO3cWXb+lzTvkGB286cORPAgjsPReku79gkit6xtflgUbKNXx6sjh51Qnv1apg0CYYN83dFxpQZC+5yQFX58zfrCRH46/D2hY45/v3339O2bVtiY/O/4ryiyuku/+GHHwrc7v7LW5B2JIPpG+zWsKBz9KjTPZ4T2jfcUPg+xgQQC+5y4LuEPczbcoBHr76ERjULHgFt586dJCQkMNSuis1TeHg4gwcPZu7cuZw5cybf7Qa3qUdMdBX++1MSzl2JwU1EhojIFhFJFJGfjVYoIpEiMtFdH++OvYCIxIhIhsd1Ku947BMhIuNEZKuIbBaRm3z4kfLm2dL+8ksLbROULLj97Oipczz77UY6No5izGUxhW7//fffIyIW3AXI6S5fuHBhvtuEhAj39YtlbeoRVqUc9mF1viciocDbwLVAO2CUO/ywp/uAw6raCngdeNFj3XaP61Qe8Fj+R2C/qsa5x817blVfOXrUGbp05UontIcP92s5xniLBbef/WP6Zg6fOsvfR3YkNI9hTT2pKt999x09evSgQYOCz4NXZH369CE6Oppvvin4TsObujchqnI4//spyUeV+U0vIFFVd6jqWeBznKGHPXkORTwJGCyFzxN7L/B3AFXNVtX0Mqy5eI4dc0J7xQoLbRP0LLj9aHnyIT5blsK9fWPo0Diq0O03btxIUlIS19vVsQUKDw/n+uuvZ968eRw+nH9rukpEGKN7N2PGhr3BPmtYY8DzEvpd7rI8t1HVTOAoEO2uixWR1SIyX0QuBxCRmu66v4nIKhH5UkTyHL7Y66MgHjvmdI/nhPaIEWX/HsaUIxbcfnI2M5unJq+jcc3K/PaquCLtM3nyZCIiIrjmmmu8XF3gGzFiBOfOnWPatGkFbveLS2MIDRH+t3CHjyoLOHuAZqraFfgd8KmI1ADCgCbAYlXtBiwBXsnrAF4dBdGzpf3FFxbapkKw4PaTd+dvZ9v+Ezw3ogNVIsIK3f706dN89913XH311URFFd46r+jatGlDmzZt+PrrrwvcrkFUJW7q1oTPl6ey/9hp3xTne2lAU4/XTdxleW4jImFAFHBQVc+o6kEAVV0JbAfigIPAKSBnKOMvgW7e+gB5OnbMmdlr+XKYOBFuvNGnb2+Mv1hw+8GOAyf419xEhnZqyMA29Yq0z6xZszh27Bg33eT/C3cDxfDhw1m/fj1bt24tcLsHB7QiK1t5d0HQtrqXA61FJFZEIoDbcYYe9uQ5FPHNwBxVVRGp617choi0AFoDO9wJgr4FBrj7DAY2evdjeDh+3AntZcvg889h5EifvbUx/mbB7WOqyh+nrCcyLIS/DMt9YW/+vvrqK5o0aUKvXr28WF1wGT58OOHh4XzxxRcFbtcsugrDuzRiQvxO0k/kfwtZoHLPWT8EzAA2AV+o6gYR+auI5Nwv9R4QLSKJOF3iObeMXQEkiMganIvWHvAY7fD3wDMikoAzvPGjPvlAx4873ePx8U5o2x+zpoKx4PaxSSt3sWTHQZ68tg31qlcq0j4pKSksXbqUkSNHEhJi/2RFVatWLYYMGcI333zDyZMnC9z2/wa24kxmdtBeYe4ONxynqi1V9Xl32dOqOtV9flpVb1HVVqraS1V3uMu/UtX27q1g3VT1W49j7lTVK9xhjQerqvdnbslpacfHO93jFtqmArIU8KEDx8/w/LRN9Ghei1E9mxV5v08//ZSwsDDrJi+B22+/nRMnThR6kVrLutW4vlMjPl6SzOGT+Y9zbvwoJ7SXLrWWtqnQLLh9RFX509frOHU2i3/c1JGQQu7ZznHy5EkmT57M1VdfTb16RTsfbi7o2rUrrVu35vPPPy90hLSHB7Xi1Lks/j0v0UfVmSI7fhyuu+5CaN98s78rMsZvLLh9ZOra3czYsI9Hr4qjVb3qRd7v22+/5fjx49x5551erC54iQijRo1i48aNrF69usBt4+pX56ZuTRi/eCe7Dgf1fd2B5cQJJ7SXLIHPPrPQNhWeBbcP7D92mqe/2UC3ZjW5//IWRd5PVfnkk09o3749Xbp08V6BQW7EiBHUrFmT999/v9Btf3dVHCLw2syCr0Q3PuIZ2p9+Crfc4u+KjPE7C24vU1WemrKO0+eyeOWWzoUOa+pp3rx5bN++nbvuuqvQGcNM/ipXrsyoUaOYM2cOSUkFX3zWqGZl7u4bw5TVaWzcfcxHFZo85YT24sUwYQLcequ/KzKmXLDg9rLJq9KYtWk/TwxpQ4u61Yq8n6ry7rvv0rhxY6677jovVlgx3HHHHYSHhzN+/PhCt32wfytqVArnxembfVCZydOJEzB06IXQvu02f1dkTLlhwe1FKQdP8ZepG+gVU5t7ijDzl6fly5ezdu1a7r33XsLDw71TYAVSp04dRowYwZQpU9i7t+A5uKOqhPPQwFbM33qAWRv3+ahCc97Jk05oL1xooW1MHiy4veRcVjYPf76aEIHXbutc5KvIc7zzzjvUqVOHkTYiVJkZO3Ysqsq4ceMK3XbMZTG0rleNZ77dQMbZLB9UZwALbWOKwILbS175cQtrU4/w4k2daFKrSrH2Xbp0KUuWLOHee++lUqWiDdJiCte4cWNGjhzJpEmT2L17d4HbRoSF8NyIDuw6nMG/5mzzUYWG1audYUw/+QRuv93f1RhTLllwe8H8rQd4d/4O7ujdjGs7NizWvqrKa6+9RsOGDbnjjju8VGHF9atf/QqA//znP4Vu27tFNDd1a8J/f9pB4v7j3i7NAPTrB0lJMGqUvysxptyy4C5jqYdO8ZvPVxNXvxp/Hlr0schz/Pjjj6xbt46HHnqIyMhIL1RYsTVs2JDbb7+dyZMns2XLlkK3/8N1bagSEcZTk9eTnV3wAC6mjNTPc1pvY4zLgrsMZZzNYuzHK8nOVsbd1YPKEaHF2v/06dO8+uqrtGzZkuHDh3upSvPggw9SvXp1/v73vxc6mlqdapH8cWhbliUf4v1FwTmOuTEmsFhwlxFV5YmvEti89xhvjupKTJ2qxT7Gf//7X1JTU/nTn/5EaGjxQt8UXc2aNXnkkUeIj49n5syZhW5/S/cmXNm2Pi/N2MLWfdZlbozxLwvuMvKvOYl8u3Y3j119CQMvKf6Y4snJyfz3v//l+uuvp0+fPl6o0Hi69dZbiYuL4/nnn+fYsYIHWhER/nFTR6pHhvHIZ6vtKnNjjF9ZcJeBictTeG3mVm7s2pgHB7Qs9v5ZWVn8+c9/JjIykscff9wLFZrcwsLCeO6550hPT+fll18udPs61SJ57bYubNl3nD9/s94HFRpjTN4suEtpzuZ9PDVlPZe3rsNLN3cq0dCk48ePZ8WKFTz11FM2A5gPdezYkXvuuYdJkyaxaNGiQrfvH1eXhwe1ZtLKXXwa7/2pp8uaiAwRkS0ikigiT+axPlJEJrrr40Ukxl0eIyIZIrLGfbyTx75TRcT+ojHGByy4S2Hx9nQenLCKdg1r8J87uxMeWvxv5+bNm3njjTe46qqrGDFiRNkXaQr08MMP07JlS37/+9+zf//+Qrf/zeDW9I+ry9PfrGdRYroPKiwbIhIKvA1cC7QDRolI7tse7gMOq2or4HXgRY9121W1i/t4INexRwInvFe9McaTBXcJLdh6gHs+WE6z2lV4/+6eVIsMK/Yxjhw5wsMPP0ytWrV49tlnbSIRP4iMjOSNN97g1KlTPP7442RmZha4fWiI8NYdXWlZtxoPfLKSLXsD5mK1XkCiqu5Q1bPA50DuWxeGAzmDuU8CBkshP5QiUg34HfBcGddrjMmHBXcJzN28n/s/WkFsnap89ss+1K1e/PutMzMzefTRR9m3bx///Oc/qVWrlhcqNUXRqlUrnn76aZYtW8Y//vGPQm8Rq14pnPfv6UmViFBG/y+eHQcCorHZGEj1eL3LXZbnNqqaCRwFot11sSKyWkTmi8jlHvv8DXgVKJMJzCdMgJgYCAlxvk6YUBZHNSa4WHAX06fxKfzyoxW0rleNz37Zh+hqxQ9tVeWZZ55h8eLFPP3003Tu3NkLlZriGDFiBHfffTcTJkwo0gxijWtWZsL9vVFV7vhvwIR3Se0BmqlqV5zW9aciUkNEugAtVXVKQTuLyFgRWSEiKw4cOJDvdhMmwNixsHMnqDpfx4618DYmNwvuIsrKVv767UaemrKOvq3q8NnYPtSqGlHs46gqL7/8Ml999RW//vWvufnmm71QrSmJxx9/nKuvvpoXX3yRL7/8stDtW9Wrzif39+ZcVja3vLOEdbuO+qDKEksDmnq8buIuy3MbEQkDooCDqnpGVQ8CqOpKYDsQB1wK9BCRZGAhECci83K/saqOU9Ueqtqjbt26+Rb4xz/CqVzt9lOnnOXGmAssuItg37HTjHl/Ge8vSuKevjG8N6YHNSoVf6rN7OxsnnvuOT744ANGjx7Nww8/7IVqTUmFhITw0ksvcfnll/P000/z6aefFrpP24Y1+PKBS6kUHsrt45YwfX3BU4b60XKgtYjEikgEcDswNdc2U4Ex7vObgTmqqiJS1724DRFpAbQGdqjqf1S1karGAP2Arao6oKQFpuRzoX5+y42pqCy4CzF9/R6ueWMBK3ce5sWbOvKXYe0JK8HV4xkZGTz66KN8+umn3HPPPTz11FN2MVo5FBkZyVtvvcXAgQP529/+xiuvvEJWVsEDrrSoW43JD15Gq/rVeeCTlbw8YzPnsrJ9VHHRuOesHwJmAJuAL1R1g4j8VURucDd7D4gWkUScLvGcW8auABJEZA3ORWsPqOqhsq6xWbPiLTemoir+pdAVRNqRDF6YtonvE/bQsXEUb97ehRZ1q5XoWMnJyfzmN79h27ZtPPbYY9x3331lXK0pSxEREbz55pu88MILvPfee2zbto0XXniB6OjofPepX6MSE8f24ZmpG3h77nZ+2pbOa7d2plW96j6svGCqOg2YlmvZ0x7PTwO35LHfV8BXhRw7GehQmvqef945p+3ZXV6lirPcGHOBtbhzOXEmkzdmbWXwq/OYtXEfv70yjq9+fVmJQjsrK4vx48czcuRI9u/fz7hx4yy0A0R4eDh/+ctf+Mtf/sLSpUu54YYb+PHHHwu84rxSeCj/uKkT/x7djdRDpxjyxk/87buNHD11zoeVB67Ro2HcOGjeHEScr+PGOcuNMRdIYbe+lAc9evTQFStWePU9Dp08y4eLkvhwcTLHTmcytGND/nBdG5rUqlLsY6kqixcv5tVXX2XTpk3079+fZ555hgYNGnih8osF4ohehbmjt3/7Srdt28bvf/97Nm3axKWXXsoTTzxBmzZtCtwn/cQZXv1xK58vT6Fm5XB+1b8lo3o1I6py8a+NKA4RWamqPbz6JqXgi99lY4JBQb/LFTq4s7KVhYnpTFq5ixkb9nI2M5tr2tfnwQGt6Ny0ZrGPd/bsWWbMmMGECRNYu3YtjRs35ne/+x3XXnutz85nW3B7R2ZmJp9//jn/+te/OHbsGAMHDuTOO++kT58+hITk33G1cfcx/v7DJn7alk7ViFBu7dmUW3s0pU2D6l75mbDgNiY4WHB7OHTyLIu3pzNn837mbznAwZNnqVklnOGdG3Fnn+a0rl+8c5Jnz55l+fLlzJ49mx9//JGDBw/SvHlz7rrrLm655RYiIop/y1hpWHB719GjR5kwYQIfffQRR48epWHDhtxwww0MHDiQDh065Dsd6/q0o7y/MImpa3eTma20rFuVoZ0a0T+uDp2a1CzRcLl5seA2JjiUu+AWkSHAm0Ao8D9V/UdB25fklz07W0k7kkHigRNs33+CTXuOszrlMDvSTwIQVTmc/nF1GdKhAYPb1iMyrPD5rzMzM0lLS2PHjh2sX7+eNWvWsHbtWk6ePEnlypXp168ft912G5deemmBrTBvsuD2jTNnzjB79my+/vprFi1aRHZ2NlFRUfTq1YsOHTrQvn17WrduTd26dS9qWR88cYbpG/by7drdxCcdQhWqRITSvXkt2jWsQVv3EVOnSpF+JnOz4DYmOBT0u+zzq8o9Jju4CmfYxeUiMlVVN5b0mF+vTiNh11H2HT/N/mOn2Xv0NPuPZXDmXKYzBJNmE101nI6NajCsbWO6NKlO2wbVyc7K5PTpDBK3bObUqVOcPn2ajIwMTp06xaFDhzh48CDp6ekcOnSIPXv2kJKSwrlzzoVGISEhxMXFMWzYMK644gouvfRSKlWqVCbfI1P+RUZGct1113Hddddx+PBhFi9ezOLFi1m+fDkzZ848v12lSpVo0qQJTZs2pU6dOtSqVYtatWpxe72ajG5ahR2Hz7H5wEm2JR8kfu05zhGKhoRDaBh1q1eiYa2qNK5dlXrVK/OLvrG0LOGdDcaY4OGP28HOT3YAICI5kx2UOLhfeeZJDiaudkNaASUEqOyxzSkg3n0UVWRkJNHR0URHRxMTE8PAgQOJjY0lNjaWuLg4qlatWtKSTRCpVasWQ4cOZejQoYDTnb5x40Z27NjBrl27SE1NZdeuXaxbt44jR47kO5FJqPvIcQLY5j4AYl54g5Y3XuPFT2KMCQQ+7yoXkZuBIap6v/v6LqC3qj6Ua7uxwFj35SXAFh+VWAcoD/M1lpc6wGrJT3mspbmq5j+uqJ+JyAFgp7/rKKby9O/sLcH+GQPx8+X7u1xuB2BR1XHAOF+/r4isKA/nCMtLHWC15MdqKb7y/EdFfgLle1sawf4Zg+3z+eMKqqJMdmCMMcaYPPgjuIsy2YExxhhj8uDzrnJVzRSRnMkOQoH3VXWDr+sogM+75/NRXuoAqyU/VkvFUBG+t8H+GYPq8wXEACzGGGOMcdgkI8YYY0wAseA2xhhjAkiFCW4RGSIiW0QkUUSezGN9pIhMdNfHi0hMrvXNROSEiDzmr1pEJEZEMkRkjft4x1+1uOs6icgSEdkgIutEpFRDx5Xi+zLa43uyRkSyRaSLn2oJF5Hx7vdjk4j8oTR1lLKWCBH5wK1lrYgMKG0tFYGI1BaRmSKyzf1aK5/tsjx+5gLiAtvS/j9Y3hXh890tIgc8/t3u90edpaaqQf/AuQhuO9ACiADWAu1ybfMg8I77/HZgYq71k4Avgcf8VQsQA6wvD98XnAsbE4DO7utoINSf/0bu8o7Adj9+X+4APnefVwGSgRg/1fJ/wAfu83rASiCkrH5+gvUBvAQ86T5/Engxn+1O+LtWX/0sBcKjiJ/vbuAtf9da2kdFaXGfH2ZVVc8COcOsehoOjHefTwIGizizQ4jICCAJKIur30tVSxkrTS1XAwmquhZAVQ+qapafavE0yt23NEpTiwJVRSQMZ9Tds8AxP9XSDpgDoKr7gSNA0AxC4UWe38/xwAj/lVKmytP/Pd5QlM8XFCpKcDcGUj1e73KX5bmNqmYCR4FoEakG/B541t+1uOtiRWS1iMwXkcv9WEscoCIyQ0RWicgTfqzF023AZ36sZRJwEtgDpACvqOohP9WyFrhBRMJEJBbozsWDH5m81VfVPe7zvUD9fLarJCIrRGSp+8d9eVdWv2PlVVE+H8BNIpIgIpNEJCB/H8rtkKflyDPA66p6ohz84bkHaKaqB0WkO/C1iLRX1dK06EoqDOgH9MSZw2W2ONPQzfZDLQCISG/glKqu91cNOH/1ZwGNgFrATyIyS91JdXzsfaAtsAJnfPDFbm0VnojMAhrkseqPni9UVUUkv3tmm6tqmoi0AOaIyDpV3V7WtZoy9S3wmaqeEZFf4fQuDPJzTcVWUYK7KMOs5myzy+3mjAIOAr2Bm0XkJaAmkC0ip1X1LV/Xos5JmjMAqrpSRLbjtHxLOsFxab4vu4AFqpoOICLTgG5ASYO7NLXkuJ3St7ZLW8sdwHRVPQfsF5FFON3TJQ3u0v68/DZnIxFZDGwtYR1BRVWvzG+diOwTkYaqukdEGgL78zlGmvt1h4jMA7rinGMtr8rid6w8K/TzqarnZ/kfzvUMAaeidJUXZZjVqcAY9/nNwBx1XK6qMaoaA7wBvFCK0C5VLSJSV5z5zHH/ym9NyQOhVLXgjHzXUUSquL/g/SnF1KylrAURCQFupfTnt0tbSwruX/AiUhXoA2z2Ry3uv01Vt5argEwtxbz3FYjn93MM8E3uDUSklohEus/rAH0p3c+/L5TqdywAFPr53D/EctwAbPJhfWXH31fH+eoBXIfT2tgO/NFd9lfgBvd5JZyrxhOBZUCLPI7xDKW8qrw0tQA34VwgtwZYBQzz5/cFuNOtZz3wkp9rGQAs9ffPC1DNXb4B5z/yx/1YSwzOdLibgFk4Xbt+/10s7w+cc7qzcaZCnwXUdpf3AP7nPr8MWIdzHcE64D5/1+3Nn6VAeRTh8/3d/d1cC8wF2vi75pI8bMhTY4wxJoBUlK5yY4wxJihYcBtjjDEBxILbGGOMCSAW3MYYY0wAseA2xhhjAogFtzHGFIOIRHvMLrVXRNLc50dExKf3covICBFp5/H6ryKS7+AyBRwnRkT8NuKgiDyV6/Vi96tf6yqvLLiNMaYY1JlQp4uqdgHewRkSuQvQBcgu6/dzBzjKzwicyWRyantaVWeVdQ0+cFFwq+pl/iokEFhwG2NM2QkVkf+KM0f9jyJSGUBEWorIdBFZKSI/iUgbd3mMiMxxJ72YLSLN3OUfisg7IhIPvJTX/iJyGc7oXy+7Lf6W7n43u8foKSKLxZmLfZmIVHff7ydxJgZa5R4jX+J4S5w5rmeJyDSP4ye7o8YhIj3cYV8RkV4iskScyZAWi8gl7vK7RWSy+zm2iTOMNCLyD6Cy+xkmuMtO5FFLqIi8LCLL3e/Xr9zlDUVkgbv/ein95EvlXkUZq9wYY3yhNTBKVX8pIl/gjHb4CTAOeEBVt4kzGc6/cYbG/RcwXlXHi8i9wD+5MI1oE+AyVc0Skdm591fVQSIyFfhOVScBiDsRkjvk50TgNlVdLiI1gAyccdevUtXTItIaZ2z/gqZ6vRG4BKdVXx9nNMD3C/kebAYuV9VMt9v+Bff7AE6vRFecORe2iMi/VPVJEXnI7bUoyH3AUVXt6Q43u0hEfgRGAjNU9XlxhoSuUshxAp4FtzHGlJ0kVV3jPl8JxIgzNfBlwJdyYYbBSPfrpTjBA/AxF0968aUb2gXtn59LgD2quhxA3RkE3bHr3xKRLjgzxcUVcpwrcGbTygJ2i8icQrYHZ2KS8e4fBgqEe6ybrapH3Vo2As25eCrOglwNdMpp8bvv0xpnjPL3RSQc+Nrj+x+0LLiNMabsnPF4ngVUxjkleaQILcrcTrpfS7p/Xn4L7AM6u8c9XYpjZXLhdGslj+V/A+aq6o0iEgPM81iX+/tTnAwS4GFVnfGzFSJXAEOBD0XkNVX9qBjHDTh2jtsYY7zIbe0micgtcP68cWd39WKcWawARgM/FXP/40D1PN52C9BQRHq6+1SXC9N07lHVbOAuILSQ8hcAt7nnlxsCAz3WJQPd3ec3eSyP4sJ0mncXcvwc59wWc0FmAL/O2U5E4kSkqog0B/ap6n9xpursVsT3DFgW3MYY432jgftEZC3O7FTD3eUPA/eISAJOkP6mmPt/DjzuXgjWMmdjVT0L3Ab8y91nJk6r+N/AGHdZGy606vMzBWeWtI3AR8ASj3XPAm+KyAqc1nOOl4C/i8hqit6iHgck5Fyclo//uXWsEucWsXfd4w8A1rrvdxvwZhHfM2DZ7GDGGGOKREQ+xONiOOMf1uI2xhhjAoi1uI0xxpgAYi1uY4wxJoBYcBtjjDEBxILbGGOMCSAW3MYYY0wAseA2xhhjAsj/B8UeYmS/sI8nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "repeat_buy = [rate for rate in train_data_user_info.groupby(['gender'])['label'].mean()] \n",
    "\n",
    "plt.figure(figsize=(8,4))\n",
    "\n",
    "ax=plt.subplot(1,2,1)\n",
    "sns.distplot(repeat_buy, fit=stats.norm)\n",
    "ax=plt.subplot(1,2,2)\n",
    "res = stats.probplot(repeat_buy, plot=plt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出男女的复购率不一样"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 查看用户年龄与复购的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "plt.title('Age VS Label')\n",
    "ax = sns.countplot('age_range',hue='label',data=train_data_user_info)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 查看用户年龄复购的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "repeat_buy = [rate for rate in train_data_user_info.groupby(['age_range'])['label'].mean()] \n",
    "\n",
    "plt.figure(figsize=(8,4))\n",
    "\n",
    "ax=plt.subplot(1,2,1)\n",
    "sns.distplot(repeat_buy, fit=stats.norm)\n",
    "ax=plt.subplot(1,2,2)\n",
    "res = stats.probplot(repeat_buy, plot=plt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出不同年龄段，复购概率不同"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
